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From Mobile Access to Use

Evidence of Feature-level Digital Divides in India

Jang Bahadur Singh (jbs@iimtrichy.ac.in) teaches management information systems (MIS) at and M Vimalkumar (vimalkumar.f15005@iimtrichy.ac.in) is a fellow at the Indian Institute of Management, Tiruchirappalli with MIS as his subject area.

 

The digital divide is the disparity between individuals with respect to access to information and communication technologies. The growing prevalence of mobile phones in India is often linked to phones becoming access points to various government schemes and services. However, ICTs have various features that are not uniformly operated by different users. The use of mobile phones is examined using micro-level data to highlight how the socio-demographic characteristics of individuals (age, gender, literacy, etc) influence their engagement with the various features of a mobile phone.

We express our sincere gratitude to the anonymous reviewer(s) for their time and constructive feedback on the initial draft of the paper, as we feel that they have helped produce a stronger manuscript.
 

Information and communications technologies (ICTs) facilitate the economic development of the poor and marginalised by improving their access to education, healthcare, and financial services (Waverman et al 2005; O’Riain 2004; Steinmuller 2001). Thus, it is important to bridge the digital divide—the inequality between the technological “haves” and “have nots” (Dewan and Riggins 2005; OECD 2001; Wei et al 2011)—and promote equitable access to ICTs such as personal computers and mobile phones.

Over the last decade, India has witnessed an exponential growth in the penetration of mobile phones, and according to recent statistics, there are 88 mobile phone connections for every 100 individuals (TRAI 2017). Mobile phones are a major channel for accessing the internet in India—approximately 81% of internet users in India access it using smartphones (Neeraj 2016). Although smartphone penetration is still low, its growth has been robust due to the recent availability of low-cost handsets (Euromonitor 2017). This has led to growing optimism that the digital divide can be bridged with mobile phones. In recent years, the government has been trying to improve access to government services by linking delivery to mobile phones and mobile internet. With policy interventions like Digital India (2015), the current government envisions a digital society in which a cashless economy will feature prominently, made possible by mobile banking and digital payments. However, the challenges associated with implementing technology-based policy interventions among a diverse population have sparked a renewed debate on their efficacy in addressing social problems (for example, Prakash 2016a; Mannathukkaren 2015).

The literature on the digital divide treats all ICTs as monolithic. However, ICTs—like mobile phones—have various features and functionalities (for example, voice calls, text messages, and internet applications) that all users may not operate similarly (Selwyn 2004; van Dijk 2012). Furthermore, research on the digital divide focuses predominantly on developed countries (Reinartz 2016; Wei et al 2011). Given the increasing reliance on ICTs to promote inclusive development in developing countries, there is a need to explore whether differences exist in feature usage among mobile phone users in these nations (Donner 2006). This paper adopts a feature-centric approach to characterise the digital divide among Indian mobile phone users.

Even though a number of government services are being delivered digitally, particularly through mobile devices, to the best of our knowledge, there is no countrywide study of India to understand the scope of the digital divide based on users’ engagement with the various features of ICTs. Thus, the objective of this study is to understand how users’ engagement with various feature categories varies in different socio-demographic groups.

Understanding the Digital Divide

In the past decade, digital divide research has received significant attention from scholars in various disciplines—for example, social sciences, developmental studies, and information systems (Reinartz 2016). The literature identifies the nature of the digital divide, its antecedents, and its consequences (Wei et al 2011).

Based on the literature review in Dewan and Riggins’ (2005) study on the digital divide, Wei et al (2011) developed a framework with three levels to help understand the extant literature. In this framework, each successive level indicates a higher order—they are digital access divide, digital capability divide, and digital outcome divide. The digital access divide focuses on inequalities in access to ICTs such as personal computers, mobile phones, and internet in the home, school, or workplace (Dewan and Riggins 2005). The digital capability divide refers to inequalities in ICT capabilities (Wei et al 2011)—“the ability to use technology” when people have access to it (Dewan and Riggins 2005: 301). The digital access and capability divides, and other contextual factors, lead to differences in outcomes caused by ICT usage, leading to a digital outcome divide (Wei et al 2011). The literature suggests that various key socio-demographic factors influence the digital access divide. Socio-economic status profoundly impacts the access divide (Reinartz 2016; Hsieh et al 2011; Goldfarb and Prince 2008). Age, in particular, can significantly influence access to mobile phones and the internet (Chen 2013; Boase 2010; Rice and Katz 2003). Along with age, income and education are important factors that affect individuals’ digital skills (Hargittai 2002). Studies show that people with higher education levels and higher incomes exhibit a greater capacity to adopt information technology (IT) services (Akhter 2003; Lindsay 2005; Bélanger and Carter 2009). Even though a few studies suggest that there is a disparity between men and women with respect to digital capability (Wei et al 2011; Sharma 2003), there is a lack of consensus on the role of gender in the digital divide in studies carried out in countries like the United States and Singapore (Reinartz 2016).

The existing research provides a rich understanding of the digital divide; however, two issues remain under-explored. First, most studies in the digital divide domain conceptualise ICTs as a monolithic black box (for example, Dewan and Riggins 2005; Wei et al 2011; DiMaggio and Bonikowski 2008), instead of considering that users may operate some features but not others. Thus, an analysis of what features are accessible to users is needed. Second, the existing studies were predominantly conducted in developed countries, and this data may not hold true for the developing world. Thus, a feature-centric study of the digital divide in the developing world is needed.

There are limited studies on the use of ICTs that address the characteristics or features of ICT devices. Scholars have identified a range of “divides” among users in their approach to different forms of ICT hardware. Pearce and Rice (2013) categorise the research on disparities among users based on hardware. For instance, based on internet usage patterns, Donner et al (2011) propose four hardware-based access categories—neither computer- nor mobile-based access, computer-based access, mobile phone-based access, and mobile and computer-based access—and show that there are differences among these various types of hardware users.

These studies offer insights on the various kinds of digital divides by exploring the differences in people’s use of various features. The use of ICT features can be studied based on hardware or software (van Dijk 2005). While there are studies on the uses of hardware features (for example, Chigona et al 2009; Donner et al 2011; Pearce and Rice 2013), to the best of our knowledge, there have been no significant efforts to understand differences in usage in terms of the software features of ICT devices, particularly mobile phones.

The software of an ICT device is essentially a combination of multiple features that various users operate differently; thus, researchers have expressed the need for a feature-centric disaggregation of technology (for example, Donner 2006; Jasperson et al 2005). Considering that the delivery of government entitlements relies on mobile phone penetration and the range of mobile phone features available to users, it is useful to disaggregate mobile phone features into voice calls, text messages, and internet applications. Such a disaggregation will lead to a whole range of studies on the adoption, access, and outcomes of ICTs as tools for social inclusion.

Categorising Mobile Phones Based on Features

In this section, a brief history of mobile phone models is presented and a framework to categorise the various features of mobile phones is proposed. Mobile phone service started in India in 1995, with the provision of basic features such as making and receiving telephone calls and short message service (SMS). First-generation mobile phones (also known as basic phones) are still popular in India due to their simplicity, low prices, and long battery lives (Agence France-Presse 2012). As technology advanced, mobile phones incorporated more features such as music players, cameras, and games. These mid-range phones are also known as feature phones. They were the first handsets to receive internet connectivity through GPRS (General Packet Radio Services), popularly known as second generation (2G) internet technology (Singh 2012).

However, the mobile phone industry massively changed with the introduction of advanced computing platforms—popularly known as mobile operating systems—such as Android, iOS, and Windows. These platforms supported mobile application development by third parties and created application delivery channels, such as the Play Store or App Store, through which users could download and install various applications. These applications range from simple utility applications like calculators and notepads to mobile games, social media applications, health monitoring applications, and applications to access government services. Currently, both Android’s Play Store and Apple’s App Store host more than two million applications each (Statista 2017). The phones that come with these mobile operating systems are known as smartphones, and they are typically capable of internet connectivity. The features of these phones can be extended through the myriad applications available through the app stores.

As explained above, mobile phones offer users various features and functionalities. These features can be grouped according to two broad characteristics—the complexity of the feature and its dependency on the internet. Using these two characteristics, we propose a framework to categorise mobile phone features into four distinct groups. Such a framework facilitates empirical analysis by helping divide a range of features into a finite set of categories.

The proposed 2*2 framework disaggregates mobile devices based on their features (Figure 1). One important characteristic of mobile devices in this era is internet dependency (van Dijk 2012). We broadly categorise the applications available on mobile phones into two kinds—applications that require an internet connection and applications that do not. For instance, activities like making phone calls and taking pictures do not need access to the internet, whereas browsing through social media and mobile banking do. Hence, the features of mobile phones can be grouped based on whether they require internet access. In the framework, we conceptualise this characteristic along the horizontal axis.

Along similar lines, the other characteristic of mobile applications considered in this framework is the complexity of the application. Using the notion of medium-related skills, van Deursen and van Dijk (2010) define complexity as the degree of effort and the set of skills required by users to access a feature. The greater the degree of effort employed or the skills required to operate the feature, the greater the complexity, and vice versa. For example, to make a phone call, users just need numeracy—they dial the number and press the call button. Thus, such features are on the lower end of the complexity scale. On the other hand, to make an e-commerce transaction on a mobile phone, users must be literate, be able to search for products, compare and choose products, add products to a virtual shopping cart, enter delivery and billing addresses, decide whether to make the payment online (in which case they must have access to bank accounts or mobile wallets), and so on. Thus, such features appear at the higher end of the complexity axis.

By arranging these features—according to complexity and internet dependency—on a 2*2 matrix, we obtain four categories of mobile phone features. The basic features (Group 1) rank low in complexity and do not require internet access. For instance, making or receiving calls does not require linguistic skills nor internet access, so it is considered low in complexity. General features (Group 2) encompass those that are high in complexity, but which do not require internet access. For example, use of SMS necessitates language and typing skills. Similarly, taking a picture with a camera phone needs the user to adjust the lighting, focus, and zoom. These features do not require internet access to function.

Social features (Group 3) are generally user-friendly applications that are comparatively low in complexity, but need internet access to operate. For instance, social networking applications like Facebook, WhatsApp, and Twitter require internet access and are relatively user-friendly, and users with fundamental language skills, with minimal effort, can easily use these applications. Finally, Group 4—advanced features—are complex and also need internet access to function. For instance, e-commerce and mobile banking applications require internet access, and users must go through multiple steps to perform a single transaction.

Based on this framework, we analysed empirical data obtained from a nationwide survey of Indian adults about their usage of various mobile phone features. The details of the empirical analysis and results are discussed in the next section.

Research Methodology

To understand whether disparities exist among individuals—with regard to their ability to use features in the four categories—an empirical study was conducted and described the following sections.

Data collection and description: This study utilises an individual-level data set from InterMedia’s Financial Inclusion Insights Tracker Survey (InterMedia 2016). The data set features information from an annual national representative survey with a sample size of 45,036 of Indian adults, aged 15 years and above. The survey was conducted between 6 March and 10 April 2015, across 29 Indian states. Its breadth of coverage is one of the data set’s strengths, as it represents people of varying ages across states (Mukhopadhyay 2016). Primarily, the survey measures trends and market developments in digital financial services, based on the first and second surveys conducted in 2013 and 2014, respectively. It used a multistage, stratified, clustered, and randomised sampling methodology, which generated a proportional distribution of samples across all Indian states, based on the 2011 Census of India. The data were collected through face-to-face interviews with individuals, each lasting an average of 49 minutes. The collected data set contained information on basic demographics, household characteristics, and access to and use of mobile devices, mobile money, formal financial services, financial literacy, and other general financial behaviour.

For our analysis, we have selected a part of the data set containing the variables of interest (see the next section for details) and we recoded them to simplify the analysis. A description of the recoded variables and a statistical summary of the data set is provided in Table 1. The acquired data set includes both, mobile phone users and non-users. This study only considers respondents with access to mobile phones. Hence, the subsample included 37,672 respondents, of whom 42.7% were male and 57.3% were female.

Dependent variables: The data set obtained describes mobile phone usage behaviour along with demographics and related information. The data includes information on the different features that individuals use such as making and receiving phone calls, sending text messages, browsing the internet, and using social networking sites. Respondents were asked whether they had used any of these features in the recent past. Based on the respondents’ reported use of features, we categorised them into four groups, in line with the proposed framework. These four feature-use categories are the dependent variables. Group 1—basic users—are those who either made or received calls in the past three months and did not perform any other mobile phone operations. Group 2—general users—are those who, apart from making and receiving calls, also sent or received SMS or MMS, opted for caller tunes, and took photos. However, they did not perform any activities that required internet access. Group 3—social users—includes those who made internet searches or used social media applications to gather information and interact with other users. Respondents who used Facebook, WhatsApp, or Twitter on their phones were categorised in Group 3. Finally, Group 4—advanced users—are those who used their phones for mobile banking, to download songs, and to perform other advanced operations. The higher order groups also used the features used by the lower order groups. Thus, Group 4 members use advanced features like mobile banking, along with some or all of the features used by Groups 1, 2 and 3.

Independent variables: Existing literature on the digital divide notes that users’ abilities affect the digital divide on different levels. For instance, studies show that socio-demographic factors like age, income, education, and area of residence influence the digital divide (Chen 2013; Hsieh et al 2011; Boase 2010; Goldfarb and Prince 2008; Rice and Katz 2003). Similarly, we believe that these variables might also affect the feature-use divide. Hence, the first set of independent variables for this analysis includes demographic variables such as age, area of residence, education, and occupation/work. Earlier studies on gender have shown inconsistent outcomes with regard to the effect of gender on the digital divide. While some studies show that men are more likely to adopt ICTs than women (Hoffman et al 2006; Meneses and Mominó 2010), others find that these effects disappear once other variables like age or income are controlled for (LaRose et al 2007; Rice and Katz 2003). These mixed findings prompted us to consider the gender of respondents as an important independent variable to study its impact on the feature-use divide.

Mobile phone ownership—the number of mobiles in a household—affects the computer self-efficacy of individuals (Wei et al 2011). Studies show that owning mobile phones enhances individuals’ self-efficacy. In contrast, the number of phones in a household indicates access to phones and the availability of resources. Thus, we have considered access to mobile phones and resource availability as independent variables. Given that most features on mobile phones use English (Prakash 2016b), users’ proficiency in the language plays a major role in their use of various applications. Similarly, users’ ability to operate phones independently is a direct measure of their self-efficacy, and vice versa (Marakas et al 1998). Thus, we consider English proficiency and users’ ability to operate phones independently as the other set of independent variables in the feature-use divide analysis. Finally, another variable we have considered is bank account ownership. We considered this variable because individuals with bank accounts are more motivated to use advanced features like mobile banking and e-commerce applications. These variables and the descriptive statistics are available in Table 1.

Analysis method and model: The present study attempts to predict the likelihood of users being categorised in the four feature-use groups, based on their demographics and other characteristics. As the dependent variable has multiple categories, we use a multinomial logistic regression to analyse the data. The multinomial logistic regression is an extension of logistic regression and is employed when the dependent variable is nominal and contains more than two categories. This regression predicts the probability of a given member belonging to a category of the dependent variable (Starkweather and Moske 2011). Independent variables can either be dichotomous (that is, binary) or continuous (that is, interval or ratio in scale). To examine the feature-use divide, we conceptualised the following model.

Equation 1: Regression Model for Feature-use Divide

α + β1 Gender + β2 Age + β3 Eng_prof + β4 Education

Groups = +β5 Ownership +β6 Work+β7NumOfMob+β8 Need_asst + β9 Area + β10 Bank_Ac + ε

Before running the multinomial logistic regression model, we winsorised the age factor—the only ratio scale variable—to three standard deviations to minimise the effect of outliers.Appendix 1 (p 67) presents the matrix for Spearman’s correlation, to test for the multicollinearity of categorical variables. There is no significant correlation among the variables.

For the multinomial logistic regression analysis, we used the “mlogit” package available in the R software. The results of the regression (equation 1) are provided in Table 2. Since the multinomial logistic regression is functionally similar to the logistic regression, to interpret the coefficient we calculated the odds ratio. Considering the log-likelihood ratio (ChiSquare value) and the significant p-value, the model is statistically acceptable. For a multinomial logistic regression, McFadden’s Pseudo R2 of 0.16 is regarded as an acceptable fit (McFadden 1977).

On analysing the data in the table, we find that gender is highly significant in predicting the likelihood of group membership. Compared to women, men have significantly higher chances of being in Group 2 as compared to Group 1, that is, the odds ratio indicates that for every three men in Group 2, two women are likely to be in Group 2. The disparity is even higher between men and women in Groups 3 and 4. Considering the odds ratio, for every four men in Group 3, there are two women. This clearly indicates that as the complexity of applications and the internet access requirement increases from Groups 1 to 4, the feature-use divide increases between male and female users. The results indicate that women are more likely to only use the voice call feature as compared to men, when controlling for other factors. Similarly, the pattern repeats for the rural and urban divide. The area of residence variable is significant and the odds ratios are greater than one. The results suggest that people living in urban areas have a higher chance of being in Group 2 compared to people living in rural areas. The likelihood of belonging to Groups 3 and 4 is even higher for urban as compared to rural residents, as the odds ratio is significantly greater than one. For every rural resident in Group 3, there are two urban residents, and for every rural resident in Group 4 there are three urban residents. These results indicate the existing feature-use divide between urban and rural residents—the divide is smallest in Group 1 and highest in Group 4.

Consistent with existing studies (Niehaves and Plattfraut 2014; Lam and Lee 2006), the analysis suggests that age is an important variable in determining the extent of the digital divide. With an increase in age, self-efficacy in the use of digital devices decreases. The results for age are reported in Table 1. The results indicate that a unit increase in age reduces the chances of respondents belonging to Group 2 as compared to Group 1. Similarly, the results show that respondents’ likelihood of belonging to Groups 3 and 4 reduce even further with age. Thus, the feature-use divide increases with an increase in age differences among individuals.

Contrary to our expectations, education and occupation do not significantly affect the feature-use divide. Although higher educational attainment was expected to significantly reduce the feature-use divide, its significance is limited to a p-value of 0.017. Occupation does not significantly affect the feature-use divide, indicating the ubiquitous nature of mobile phones.

Two interesting results emerged regarding proficiency in English and mobile phone ownership, both of which affect the feature-use divide. Given that most applications on mobile phones, and the majority of content on the internet, are available in English, proficiency in the language becomes important. Hence, consistent with expectations, the results suggest that a person more proficient in English is more likely to be in Groups 3 or 4, as compared to people with lower proficiencies. Similarly, owning mobile phones might increase people’s self-efficacy, as it may motivate them to try new and more applications. The results indicate that people who own mobile phones are significantly more likely to be in the advanced feature-use category (Group 4), as compared to people who do not own mobile phones. Thus, mobile phone ownership can assist in bridging the feature-use divide.

Other variables considered in the study were the number of mobile phones in households and people’s dependency on others to use mobile phones. We found that these variables were not significant predictors of membership in Group 2, but were highly significant for Groups 3 and 4, as compared to Group 1. This indicates that when there are more mobile phones in households and more family members using phones, it leads to vicarious learning (Wei et al 2011) that helps the focal people learn to operate more complex applications. Individual independence in terms of operating mobile phones is significant, as people who can operate mobile phones independently are more likely to be in Groups 3 or 4, as compared to those who need assistance to use phones.

Finally, the holding a bank account variable is also significant and has an odds ratio greater than one. This indicates that compared to Group 1, having a bank account increases the chances of people belonging to advanced feature-use groups (Groups 2, 3 and 4), as compared to those who do not hold bank accounts.

The following sections discuss the implications on research and policy suggestions in light of our findings.

Discussion and Policy Implications

The Prime Minister, at the Digital India dinner, 2015, declared, “We have attacked poverty by using the power of networks and mobile phones to launch a new era of empowerment and inclusion” (Press Information Bureau 2015a).

The Prime Minister’s words echo the strong supposition of the government that the digitisation of welfare services can improve their reach among the poor and marginalised, and, particularly, that mobile phones and the internet can help to elevate the quality of life of the marginalised. Thus, services, such as e-government, financial inclusion, direct benefit transfer, and mobile money, are centred on digital technologies. However, this study reveals that mobile phone usage is not uniform, and, thus, the disparity among users can constrain the government in achieving the desired outcomes. The study provides evidence of the differences in feature use between individual mobile phone users based on different socio-demographics characteristics, such as gender, age, place of residence, and education. Thus, to achieve more inclusive governance, the government needs a deeper understanding of the digital divide among mobile phone and internet users. Without such an understanding of the digital divide, the efforts of the government to empower marginalised sections of society will not materialise as expected. This paper attempts to enhance our understanding of the digital divide in terms of feature use.

Mannathukkaren (2015) argues that development aimed at the marginalised and impoverished by simply providing them the mobile and internet services to access government services is just “dilution,” as they have not caught up with technological advancements. Observing the widespread presence of mobile phones but not development, Prakash (2016a) claims that the features and facilities that the government offers in the form of digital services are accessible only to certain sections of society. Anecdotal evidence shows that the demonetisation of 2016 did not affect many urban residents, especially since they tend to shop using credit/debit cards and use app-based taxi services. However, people residing in villages—who buy groceries from pushcarts, travel by autorickshaw, and use their phones just to make calls—suffered as a result of this monetary upheaval (Prakash 2016b). The results of this study demonstrate a disparity among users belonging to different feature-use categories, based on their socio-demographic profiles. This following section focuses on the implications of our findings for public policy design.

Gender inequality is a grave concern in India, which ranked 125 out of 159 countries on the Gender Inequality Index (GII) in 2015 (HDR 2016). This is apparent in the use of mobile phone features. Women are adversely affected by the feature-use divide and primarily use basic features like voice calls. Thus, government policies should encourage or incentivise women to utilise more advanced applications. Anecdotal evidence shows that encouraging women to use ICTs can be advantageous, as they provide women with opportunities to upgrade their learning and self-employment prospects (Viswanath 2017). Government initiatives such as the Pradhan Mantri Gramin Digital Saksharta Abhiyan (launched in 2017), which intends to educate citizens to use digital technologies, must focus on educating women more effectively.

Even though internet connectivity is rapidly expanding, driven mainly by mobile data plans, the growth is seemingly inequitable between urban and rural regions. According to the Telecom Regulatory Authority of India (TRAI), by the end of 2016, 61.9% of people in urban India and only 13.7% of those in rural India had internet subscriptions (Krishnan 2017). The disparity between rural and urban residents indicates a digital divide. Our findings reconfirm this feature-use divide. To make Digital India services accessible to rural residents, there must be a strong focus on building better network infrastructure in rural areas and making mobile internet connections affordable. BharatNet, an ambitious project of Digital India, aimed at establishing network infrastructure in rural India, envisaged equipping one lakh gram panchayats with broadband connectivity by March 2017 (BBNL 2016). However, as of March 2019, 43,179 gram panchayats have been connected, of which only 12,740 were operational (BBNL 2019).

Another significant factor affecting the digital divide is literacy, particularly English proficiency. Much of the content on the internet is in non-Indian languages. This results in the consumption of digital technologies being highly skewed towards those fluent in English. Rural residents who rely on vernacular languages are limited to making voice calls and consuming audiovisual entertainment available on the internet. Though efforts are being made to translate applications available in English to local languages, developers usually assume that applications specifically intended for marginalised users need not be appealing and user-friendly (Prakash 2016b). This results in low-quality and low-cost applications for rural residents. To overcome such disparities, developers should design applications with an understanding of the capabilities and needs of consumers.

Finally, owning mobile phones is a major factor that can reduce the feature-use divide across the different age groups. Our analysis suggests that people who own mobile phones utilise more features than those who borrow phones from others. Owning mobile phones allows people to experiment with services and try new features.

Policy interventions targeting weaker sections of society, such as Digital India and the drive for cashless transactions, rely on internet-dependent mobile phone features. This paper reveals that most marginalised respondents tend to use mobile phones only for voice calls (Tables 1 and 2). This means that the intended beneficiaries are the least capable of using the features through which services are delivered, which might reinforce or even worsen inequality.

While digitisation has its benefits, the use of mobile phones to deliver government services must consider the socio-economic barriers that restrict ordinary citizens from gaining access to these services. The major barriers to gaining access are the affordability of advanced technologies and the lack of digital education. Programmes like the National Digital Literacy Mission should incorporate feature literacy to overcome the divide. A good example is the decision to use voice-based interventions in the Jaankari (information) Project of Bihar, which allows illiterate people to register right to information (RTI) applications via mobile phones.

Conclusions

Considering the significance of mobile phones in developing countries, this paper attempts to enhance our understanding of the digital divide. It demonstrates that people with access to technology may not be able to use all its features equitably. This study develops a framework to disaggregate the features into four categories—basic, general, social, and advanced. Then, using large-scale survey data, the study reveals the existing disparity among Indian mobile phone users—the feature-use divide. This is an exploratory study that utilises secondary survey data that was originally collected for a different purpose. The major limiting factor of the study was that the secondary data did not capture factors such as income, which might impact the feature-use divide. Hence, future research should consider a large-scale study focused on the feature-use divide, which may uncover more explanatory variables.

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Updated On : 12th Aug, 2019

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