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Digital Natives and Digital Immigrants Towards a Model of Digital Fluency

Digital natives are the new of young people born into the digital age, while “digital immigrants” are those who learnt to use computers at some stage during their adult life. Whereas digital natives are assumed to be inherently technology-savvy, digital immigrants are usually assumed to have some difficulty with information technology. The authors suggest that there is a continuum rather than a rigid dichotomy between digital natives and digital immigrants, and this continuum is best conceptualized as digital fluency. They propose a tentative conceptual model of digital fluency that outlines factors that have a direct and indirect impact on digital fluency.

DOI 10.1007/s12599-013-0296-y

accepting new information technology, The Authors Electronic Supplementary Material then some of the assumptions of these The online version of this article theories used in IS research are thrown (doi: 10.1007/s12599-013-0296-y) into question. Qian (Emily) Wang, B. Com (Hons) contains supplementary material, Prof. Michael D. Myers, Ph.D. () However, rather than seeing the dif- which is available to authorized ference between digital natives and digi- David Sundaram, Ph.D. users. Department of Information Systems tal immigrants as a rigid dichotomy, we suggest that this difference might be best and Operations Management © Springer Fachmedien Wiesbaden conceptualized as a continuum. Some University of Auckland Business 2013 School people are more technologically adept Private Bag 92019 than others (Nedbal et al. 2012). Hence, Auckland, 1142 the research problem that we seek to ad- New Zealand 1Introduction dress in this paper is: How can we best [email protected] conceptualize technology adeptness? [email protected] We propose that the best way to con- [email protected] It has been suggested that there is a sig- nificant difference between “digital na- ceptualize this continuum of technology adeptnessisintermsofdigitalfluency. Received: 2012-08-08 tives” and “digital immigrants”. Digital natives, a generation of young people Digital fluency is the ability to reformu- Accepted: 2013-07-08 late knowledge and produce information Accepted after two revisions born into the digital age, are assumed to express oneself creatively and appro- by Prof. Dr. Buxmann. to be inherently technology-savvy (Pren- sky 2001a; Tapscott 1998). Digital immi- priately in a digital environment. There- grants, by contrast, are those who learnt fore our research question is: what are This article is also available in Ger- to use computers at some stage during the factors that have a direct and indi- man in print and via http://www. their adult life. Digital immigrants are as- rect impact on digital fluency? The pur- wirtschaftsinformatik.de: Wang Q(E), sumed to resist new technology or at least pose of this paper is to propose a tenta- Myers MD, Sundaram D (2013) Dig- have some difficulty accepting it (Vo- tive conceptual model that captures the ital Natives und digital Immigrants. danovich et al. 2010). Since IS researchers most important factors affecting digital Entwicklung eines Modells digitaler have traditionally conducted empirical fluency. Gewandtheit. WIRTSCHAFTSINFOR- research on “digital immigrants” – and The contribution of this paper is that it MATIK. doi: 10.1007/s11576-013- some of the theories such as the Theory moves the debate forward about the sup- 0390-2. of Planned Behavior (Ajzen 1991)and posed differences between digital natives the Technology Acceptance Model (Davis and digital immigrants. Based on a re- 1986) are based on the assumption that view of the state-of-the-art research on users tend to resist or at least have some this topic from multiple disciplines, we difficulty accepting new technologies and identify the relevant factors that might systems – the rise of a new generation influence digital fluency. of digital natives has profound implica- This paper is organized as follows. The tions for IS research (as well as research next section describes the research back- in other disciplines). If the new gener- ground. Section 3 discusses the method- ation of young people has no problem ology used for the systematic review. This

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is then followed by a discussion of the Li and Ranieri 2010), computer self- 2.Thekeywordswereconstructedwith evidence base and the main themes that efficacy (Compeau and Higgins 1995)or operators into search strings and emerged from our analysis of the liter- Information and Communication Tech- tested for accuracy in the search en- ature. In Sect. 6,weproposeatentative nology (ICT) competency (Guo et al. gine. conceptual model of digital fluency. The 2008). While these terms are sometimes 3. The search string was used to search in final two sections are the discussion and used interchangeably, we suggest that the the databases mentioned earlier. conclusions. concept of “digital fluency” might be 4. The search string was altered to in- the best way to conceptualize the differ- clude “generation-Y”, which is fre- ence between digital natives and digital quentlyusedasavariationof“genera- 2 Research Background immigrants (Wang et al. 2012). tion Y”.Similarly, asterisks were added Digital fluency can be defined as “the to cater for singular and plural forms. ability to reformulate knowledge to ex- ∗ In this section we discuss some of the pre- “Tech ” was added to avoid confusion press oneself creatively and appropriately, vious research on digital natives and dig- with language or other domains’ flu- and to produce and generate information ency, competence and literacy. Instead ital immigrants and propose the concept rather than simply to comprehend it” of digital fluency. of using “digital” as a keyword, we (National Research Council 1999, p. viii). found that “technology” and its vari- This goes beyond the notion of digital lit- ations are more frequently used in ab- 2.1 Digital Natives and Digital eracy, which focuses on teaching learners stracts and titles. The enhanced search Immigrants to make syntactically correct expressions stringwasformulatedasbelow: (National Research Council 1999). It im- [(digital native∗ OR digital immi- Most of the previous research on digi- ∗ plies that being digitally fluent not only grant OR net generation OR millen- tal natives and digital immigrants tends ∗ involves knowing how to engage with nial OR generation Y OR “gene- to assume that these groups are mu- ∗ technology, but also be able to produce ration-Y”) AND tech AND (com- tually exclusive cohorts. A sharp gener- ∗ ∗ things of significance with technology peten OR literacy OR fluen )]. ational boundary is assumed in much (Papert and Resnick 1995). This paper 5.Wheresupportedbythesearchen- of the literature (Jones and Czerniewicz proposes a tentative conceptual model gine, the result was filtered by peer re- 2010, p. 317). There are two character- that outlines factors that can have a di- viewed articles. We added the search rect and indirect impact on digital flu- istics commonly used to define the dif- criterion to be 1999 and onwards to ency. Our focus on digital fluency is with ferencebetweenthetwo:ageandacces- reflect the research around the digi- respect to technology usage in general, sibility. Although the exact cut-off year of tal natives area because (1) the term rather than on any specific technology birth varies, most suggest the cut-off date “digital natives” was first used in 2001 (e.g. Facebook). is somewhere between the end of 1970s to (along with the term “digital immi- the end of 1990s. grant”), and hence we captured all However, this binary view has attracted 3 Methodology the articles using these terms and criticism (Brown and Czerniewicz 2010; those immediately preceding their in- Jones and Czerniewicz 2010). One prob- troduction, (2) this was immediately lem with this view is that there are many The purpose of a systematic literature re- view is to explore and understand the ex- after the term “net generation” was young people in some parts of the world coined but before the term “digital na- with no access to technology and hence isting research in a field of study (Huff 2008). The initial phase of our systematic tives” was introduced, and (3) “mil- they can hardly be described as digital na- lennial” and “generation Y” are of- tives. Another problem is that accessibil- review was limited to digital natives, dig- ital immigrants and their digital fluency. ten used to describe the generation ity to technology does not guarantee bet- after , and the focus of ter technology usage (Ching et al. 2005, Furthermore, we focused on the domains of education, IS and computer assistant these two terms were not necessarily p. 394; Li and Ranieri 2010, p. 1041). learning, and extended it to technology related to technology in previous re- Hence, some have suggested that it might and computer science in general. Key- search; hence, we did not opt to use be better to think of digital nativity as word searches were made on databases the years these two terms were coined. a continuum (Vodanovich et al. 2010, related to the selected subjects including Our search was applied on citations p. 711). Following this line of thought, Infomit, ProQuest, EBSCO, Ovid, SAGE and abstracts where available. we propose that the concept of digi- publications and Reed Elsevier databases. 6. All search results were exported to tal fluency might be a better way to We followed the paper selection guide- reference management software for conceptualize this continuum. lines from Pittaway et al. (2004, pp. 138– further analysis. 143). The steps are outlined below: 7. Duplicates and citations without an 2.2 Digital Fluency 1. The keywords were generated based author were removed manually from on our research topic. For the dig- the software input dataset. Various terms have been used to describe ital natives related keywords, we in- 8. The citations were then reviewed ac- one’s capability, competence or skill cluded digital natives, digital immi- cording to our inclusion and exclusion in using information technology such grants, net generation (Oblinger and criteria (Appendices I and II; online as (Gilster 1997), com- Oblinger 2005; Tapscott 1998), millen- available at http://link.springer.com). puter literacy (Ktoridou and Eteokleous- nial (Strauss and Howe 1992)andgen- The main criterion for including a Grigoriou 2011), Information Technol- eration Y (Perillo 2007). Similarly, dig- journal paper is that the paper de- ogy (IT) literacy (Ferro et al. 2011), ital literacy, competence and fluency are scribes both digital natives/digital im- digital competence (Calvani et al. 2009; used as keywords for digital fluency. migrants and digital fluency. Two

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Table 1 Number of citations at each review stage Table 3 Articles per year

Step Description Included Excluded Year No. of papers % of sample

5 Database search 526 2003 1 2.78 % 6 Export to reference management software 526 2005 2 5.56 % 7 Remove duplicates and articles without authors or proper title 430 96 2008 2 5.56 % 8a Title analysis 222 208 2009 1 2.78 % 8b Abstract analysis 109 113 2010 17 47.22 % 8c A list (37), B list (40), C list (31) 37 2011 13 36.11 %

stages were undertaken to reduce the Table 2 Empirical findings by partici- in 2010 and a special section on the net number of citations. We first analyzed pant type generation in the Journal of Computer As- the titles of articles according to the sisted Learning in 2010. We also notice Participant type No. of papers exclusion criteria. Following a further that this topic started to appear in the IS abstract analysis, we applied both in- literature from 2010 with two articles in University student 14 clusion and exclusion criteria and, ac- Information Systems Research. cording to their relevance, papers were Senior school student 9 separated into three lists; A (37), B Primary school student 4 (40), and C (31). List A contained ar- Preservice teacher 3 5 Thematic Review ticles that are most relevant for the re- High school student 3 After carefully selecting the evidence view, followed by lists B and C. How- Senior (55+)2 ever, in using this approach, there ex- base, we then performed a keyword anal- ists a risk that articles may be miscat- General population 1 ysis on these papers. A keyword analy- egorized if their abstracts are poorly Unemployed (21–55) 1 sis illustrates the nature of the papers re- written. Parents of 6th grader 1 viewed for this study. After consolidation, 9. In order to provide a structured re- University staff 1 the top categories of keywords are educa- view process, two further article anal- tion level, participant type, , ysis steps were taken. First, the arti- Note: preservice teachers are enrolled students, IS type, gender, IT literacy/fluency, digi- cle keywords and abstracts were exam- however their ages vary significantly tal natives/net generation, ethnicity, Inter- ∗ net, self-efficacy, digital immigrants and ined; this allowed key themes to come 3 studies include two types of participants to the fore, and provided a holistic diffusion and adoption. Several themes view of the evidence base. Secondly, emerged from the keyword analysis as all articles were reviewed to ensure pa- on student participants of different ages. shown in Table 4. pers were categorized into the most Hence caution is needed when seeking An investigation of the search key- relevant theme. to generalize the conclusions from this words in the A list shows that terms study to older . For example, such as “millennial”, “generation Y” or the largest proportion of participants, “generation-Y” are used less frequently in these papers compared with “digital 4TheEvidenceBase university students, tend to be of a higher socio-economic background, hence they natives”, “digital immigrants” and “net may not be representative of the broader generation”. Furthermore, they are rarely In this section we discuss the evidence used in the abstract or title. This may be base that was used in our literature re- population (Bradley et al. 2008). The lack of research in the private sec- because the latter terms are tightly linked view. Table 1 highlights the number of tor may be due to the fact that the ma- with technology whereas the former are entries relevant to the subject at each jority of digital natives were in schools at more generic, generational terms. stage of the review. The result shows that the time. However, as they have started Several themes emerged from our key- studies involving digital natives and digi- to join the workforce in recent years, a word analysis. Most papers focus on the tal fluency are primarily in the education future opportunity will be to investigate study of digital divide, specifically ex- field. The top two journals contributing their behavior and compare them with ploring the determining factors and im- to the review are Computers & Education digital immigrants. pact of digital divide. Another large pro- (24 %) and Information, Communication portion of papers examine the individ- & Society (11 %). Out of all 37 papers, 4.2 Trend Analysis ual’sbehaviorwhenusingIS,orpat- one paper is a literature review and has tern of using IS. For example, many IS no empirical data. Consequently, it was Table 3 shows the articles by year of pub- applications such as computer mediated excluded from our subsequent analyses. lication. It is clear that this subject of communication, social network software, study and the evidence base is very re- Wikipedia, Twitter, and user generated 4.1 Participant Type Analysis cent, with more than 80 % of the papers content (UGC) are found in the keyword published between 2010 and 2011. More- analysis. A smaller proportion specifi- Table 2 highlights the breakdown of par- over, there is one special issue on “Learn- cally targets IS use for educational pur- ticipant types involved in the studies. As ing, the Net Generation and digital na- poses. The remaining papers belong to IS can be seen, most of the papers focus tives” in Learning, Media and Technology adoption and diffusion research.

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Table 4 Thematic analysis of papers reviewed

Coding Theme Description No. of articles % of sample

1 Digital divide Research on the gaps between individuals, household or 14 37.8% societies with regard to their technology accessibility, use and competence for a wide range of activities. 1. 2 Digital competence Studies that focus on technological competence, especially 410.8% related to cognitive perspective, processing and verifying credibility of information. 2 Pattern and preference of IS use These papers look at individuals’ IS use and behavior related to 821.6% IS use, especially based on different types of IS and users’ preference and patterns of use. 3 IS use in education Studies that investigate students’ use of information and 410.8% communication technology (ICT) in education. 3.2 ICT integration Studies that focus on issues and changes required in order for 38.1% ICT to properly integrate in education for interactive teaching and learning activities. 4 IS adoption and diffusion Research which focuses on the adoption and diffusion of IS. 4 10.8%

5.1 Digital Divide and Digital software (Wei et al. 2011, p. 171). This research project into how digital fluency Competence view tends to neglect the influence of dig- differs between digital natives and digital ital fluency (Ferro et al. 2011). Digital ac- immigrants. The concept of “digital divide” is a fre- cess is obviously a prerequisite for gain- quently discussed topic in both politi- ing digital fluency, but is not in itself suf- 5.1.3 Digital Outcome Divide cal and academic fields. Digital divide is ficient to determine one’s digital fluency sometimes referred to as digital inequal- (Fischer 2005). Extending the digital divide framework ity, but inequality of what? Initially it was from Dewan and Riggins (2005), Wei defined with respect to computer owner- 5.1.2 Digital Skill and Use Divide et al. (2011) add a third level of digi- ship or basic access to the (Bar- tal outcome divide based on studies that ron et al. 2010, p. 178), but now has a The binary view of the digital divide show that students with lower computer wider scope. Although there is no agree- was perhaps to be expected at the begin- self-efficacy have poorer learning out- ment as to its definition, extent, or im- ning of the technology diffusion process. comes. Zhao et al. (2010) echo similar pact (Dewan and Riggins 2005, p. 299), However, the declining cost of ICT made sentiments, where students with high lev- we briefly outline the evolution of the it more accessible. Therefore, researchers els of Internet self-efficacy exhibit more digital divide debate below and illustrate shifted their emphasis to the skills and exploratory behaviors. Using the Inter- how it is related to our research. use of digital technology (Goode 2010, netatschoolandhomeresultsinbetter p. 499). This divide refers to the in- academic performance than those with lower self-efficacy. 5.1.1 Digital Access Divide equality of IS capability or “the ability to use technology” and is considered as 5.2 Patterns and Preference of IS Use As the popularity of the Internet grew a second-level digital divide (Kvasny and rapidly during the mid-1990s, policy Keil 2006). Van Dijk and van Deursen Many researchers investigated users’ makers and social scientists worried (2008, p. 279) explain four types of dig- preferences and behaviors based on about the distribution of Internet access ital skills, namely instrumental skills, for- technology-based activities. These pa- (Dimaggio and Hargittai 2001, p. 141). mal digital skills, informational skills and pers show that one’s digital fluency varies At this stage, digital divide was seen di- strategic skills. Although the physical ac- significantly from one activity to another chotomously as a simple distinction be- cess divide seems to be closing in most and digital natives are not a homoge- tween “haves” and “have nots”. Since the developed countries, the digital use and neous group (Grimley and Allan 2010; National Telecommunications Informa- skills divide seems to have widened (van Hosein et al. 2010; Malliari et al. tion Administration published its first re- Dijk 2006, p. 225). Digital fluency is both 2011). However, there are commonali- port “Falling Through the Net: A Sur- a determinant of the digital divide and a ties amongst digital natives in activities vey of the Have Nots in Rural and Ur- divide in itself (Ferro et al. 2011,p.4). such as text messaging, instant messaging ban America” in 1995, many analyses It is often included as a dimension in and social networking (Kaare et al. 2007; have been written on the inequalities of digital divide models (van Dijk and van Valtonen et al. 2010). This may be due accessibility (Hargittai 2002). Deursen 2008;Ferroetal.2011). Stud- to the fact that social networking tools The meaning of “access” varies from ies have covered its definition (Huffaker gained their popularity mainly over the study to study, but generally refers to 2005), its measurement (Li and Ranieri past decade. It is also worth noting that whether one has the means to connect 2010), its correlated factors (Jones et al. resistance towards new technology is not to the Internet (Dimaggio and Hargittai 2010; Kennedy et al. 2010)anditsimpact universal among digital immigrants; the 2001, p. 2). This level of divide includes (Goode 2010). This concept of a divide data show that some of them also “love” both hardware access as well as use of related to skills is closely related to our new technology (Waycott et al. 2010).

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5.3 Education factors that have a direct and indirect im- (Nasah et al. 2010, pp. 542–543), sharpen pact on digital fluency and hence indi- programming language expertise (Nasah There has been growing interest in the cates how someone’s digital fluency can et al. 2010, p. 540), or use technologies role that ICT can play within educa- be improved. The model is based on an in general (Hosein et al. 2010, p. 404). tion (Grimley and Allan 2010;Hosein analysis of those factors that have been Traditionally, demographic and socioe- et al. 2010; Malliari et al. 2011). This found to be important in our state-of-art conomic status factors are considered as not only concerns hardware and soft- literature review on this topic from mul- the main determinants of the digital di- ware but also the teachers’ ability to tiple disciplines. If one or more studies vide (Ferro et al. 2011,p.8).Theso- use and transfer knowledge with ICT. mentioned a factor as being a significant cioeconomic status is predictive of tech- Other researchers focused their studies or insignificant contributor to digital flu- nology use (Ching et al. 2005), sophis- on the relationship between technology ency, then that factor was included or not tication of usage (Ferro et al. 2011), skills and academic performance (Luu included in our model as the case may be. and activities (Hargittai 2010). For exam- and Freeman 2011; Papastergiou et al. Our model incorporates seven factors: ple, people from more privileged back- 2011;Selwyn2008). Based on a hypoth- demographic characteristics, psychological grounds use the Internet in more in- esized ICT-scientific literacy relationship, factors, social influences, educational fac- formed ways for a greater number of Luu and Freeman (2011) suggest that stu- tors, behavioral intention, opportunity and activities (Hargittai 2010,p.92).How- dents with prior ICT knowledge, more actual use of technology.Weacknowledge ever, a New Zealand study shows that low Internet surfing experience and basic ICT that conflicting results for many of these socioeconomic pre-teens choose to per- self-efficacy earn higher scientific literacy factors have been observed in the litera- form technology related activities equally scores. This suggests there is some bene- ture. In addition, the literature indicates if not more than high socioeconomic fit in promoting the integration of ICT in that some factors are correlated, that is, counterparts (Grimley and Allan 2010). education. they may have influences on each other Ethnicity and nationality are also found as well as direct impact on digital fluency. to be important influences, but the differ- 5.4 IS Adoption and Diffusion This further complicates the research ences seem to be more related to socioe- area. Table 5 summarizes the results of conomic status (Volman et al. 2005), op- Adoption and diffusion is an important the characteristics analysis. The “Not sig- portunities of technology usage (Hargit- topic in the IS field. The Technology Ac- nificant” and “Significant” columns in- tai 2010;Ferroetal.2011), and ability to ceptance Model (Davis 1989)iswidely clude references to the papers where their speak English (Ferro et al. 2011, pp. 5–6; used in the IS acceptance literature and authors or research result shows that the Gudmundsdottir 2010, pp. 175–177). has been tested under many contexts related characteristic has or has no sig- (Davis 1989;Koufaris2002;Mooreand nificant impact on one’s digital fluency, 6.2 Educational Factors (Organizational Benbasat 1991). The TAM model sug- competence and/or literacy. Factors) gests that the perceived usefulness and 6.1 Demographic characteristics perceived ease of use influence one’s de- Some studies show that students’ dig- cision on adoption of a new technology. ital fluency differs according to educa- Age is one of the determinants used to tional factors, for example, school (Li For example, Hargittai and Litt (2011) differentiate between digital natives and and Ranieri 2010), university mode of look at the adoption of Twitter. They find digital immigrants. Some studies show study (Hosein et al. 2010), and support that the acceptance of Twitter is not ran- that age is significantly and inversely re- of computer learning at school (Goode domly distributed, but rather, an interest lated to digital fluency (Li and Ranieri 2010, p. 508). Some schools provide bet- in celebrities and entertainment news is 2010; Salajan et al. 2010). Yet, when ter technology activities to promote the an important predicator of Twitter use. including participants with wider age technology skills building than others (Li In addition, Twitter’s service is offered group ranges, the results suggest oth- and Ranieri 2010). From a social net- through many channels such as the web, erwise (Guo et al. 2008;Hoseinetal. working perspective, students that have mobile phone or text message; hence, its 2010). Keyword analysis shows that gen- more technology skilled classmates are ease of use has enhanced its adoption der, gender studies and gender differences at an advantage as interest and expertise rate. appear as keywords in 9 papers. Stud- might be shared informally (Barron et al. In summary, our thematic review re- ies show some level of gender difference 2010, p. 185). The educational factors garding this topic has shown that four key within the digital natives group (Hosein provideinsightsintohowtheexternal themes have emerged in the academic lit- et al. 2010; Tømte and Hatlevik 2011). environmental factors might affect one’s erature: digital divide and digital compe- Gender differences also exist in the in- digital fluency. tence; patterns and preference of IS use; tention towards technology use and self- education; and adoption and diffusion. confidence in technology use (Volman 6.3 Psychological Factors et al. 2005). In many ways, people in so- ciety communicate and reinforce gender- Psychological factors such as computer 6 A Conceptual Model of Digital based stereotypes (Martin et al. 1995). anxiety, computer self-efficacy and aging Fluency For example, females are found to use anxiety are barriers that can stop seniors ICT for educational purpose more often from using technology (Jung et al. 2010). Following our thematic review of the rel- (Selwyn 2008, p. 18) and are more in- On the other hand, intrinsic personal in- evant literature, we are now in a position terested in design oriented activities (Sel- terest is a motivation for people to im- to propose a tentative conceptual model wyn 2008). On the other hand, males prove their technological knowledge. In ofdigitalfluency.Thismodeloutlines are more likely to play computer games more generic technology-based activities

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Table 5 Characteristics analysis

Characteristics Not significant Significant

Demographic characteristics Age Guo et al. (2008), Hosein Li and Ranieri (2010), Salajan et al. (2010) et al. (2010) Gender Barron et al. (2010), Ching et al. (2005), Ferro et al. (2011), Hargittai (2010), Hosein et al. Volman et al. (2005) (2010), Li and Ranieri (2010), Tømte and Hatlevik (2011), Volman et al. (2005) Socio-economic status Yavuz et al. (2011), Ching et al. (2005), Ferro et al. (2011), Hargittai (2010), Hargittai and Litt Grimley and Allan (2010) (2011) Ethnicity/Nationality/Country Hargittai (2010), Hargittai and Litt (2011), Hosein et al. (2010), Tømte and Hatlevik (2011), Volman et al. (2005) Geography (i.e. urban/rural) Ferro et al. (2011) Language (barrier or ability to speak a Ferro et al. (2011), Gudmundsdottir (2010) foreign language) Size of household Ferro et al. (2011)

Educational factors University/school Li and Ranieri (2010), Barron et al. (2010) Discipline/Subject/Faculty Malliari et al. (2011)Yavuzetal.(2011) University mode of study Hosein et al. (2010) Computer supported learning Goode (2010)

Behavioral intention to use technology Behavior intention to use Sykes et al. (2009) Attitude towards technology Ktoridou and Eteokleous-Grigoriou (2011), Ferro et al. (2011)

Psychological factors Interest Hargittai and Litt (2011) Personality Malliari et al. (2011) Computer anxiety Jung et al. (2010) Aging anxiety Jung et al. (2010) Perceived ability to use technology Malliari et al. (2011)

Social influences Family and peer influence Goode (2010), Kaare et al. (2007), Thornham and McFarlane (2011), Zhao et al. (2010) Teachers’use,ability,influence Cottenetal.(2011), Gudmundsdottir (2010)

Opportunity Accessibility Ching et al. (2005), Li and Goode (2010) Ranieri (2010) Home access Ching et al. (2005)Barronetal.(2010), Wei et al. (2011) Location of access Zhao et al. (2010) Years of computer ownership Ching et al. (2005)

Use of technology Generic technology experience Li and Ranieri (2010) Hosein et al. (2010), Malliari et al. (2011), Papastergiou et al. (2011), Volman et al. 2005) Specific technology experience Barron et al. (2010), Cotten et al. (2011), Yavuz et al. (2011) Training (application specific) Malliari et al. (2011) Ktoridou and Eteokleous-Grigoriou (2011)

Type of technology Type of technology used Hosein et al. (2010), Luu and Freeman (2011) User profiles/groups Tømte and Hatlevik (2011), Valtonen et al. (2010), Grimley and Allan (2010)

Business & Information Systems Engineering BISE – STATE OF THE ART

Fig. 1 Conceptual model of digital fluency

such as information seeking tasks, per- to use technology as “digital strangers”. 2011), and specific technology-based ac- sonal characteristics are less influential Likewise, Goode (2010) discovers that the tivities (Cotten et al. 2011; Papastergiou (Malliari et al. 2011). student participants with limited home, et al. 2011). The positive relationship be- school computer access and support from tween frequency and fluency remains un- 6.4 Social Influences others would continue to suffer from low til the user reaches optimum efficiency digital fluency throughout high school (Hosein et al. 2010, p. 415). Social influences of peers and others on and university. Students with home In- one’s proficiency of technology use are ternet or computer access have the high- 6.8 Type of Technology important (Eckhardt et al. 2009; Laumer est self-efficacy (Zhao et al. 2010;Wei et al. 2010). Social influences can be et al. 2011)andareabletoconductmore Researchers have tried to move the fo- from family (Goode 2010; van den Beemt sophisticated tasks (Barron et al. 2010). cus towards types of activities instead of et al. 2010; Zhao et al. 2010), peers particular technologies (Kennedy et al. (Kaare et al. 2007), superiors (Zhao et al. 6.6 Behavioral Intention to Use 2009; Malliari et al. 2011). Many large- 2010)andteachers (Bennett and Ma- scale studies show that except for so- ton 2010). Among these influences, social There is a substantial body of empiri- cial networking, web 2.0 related activ- support from school has a greater effect cal support for the relationship between ities are less understood and less en- on teenagers than other forms of social behavioral intention and actual behav- gaged in by digital natives (Kennedy et al. influence (Zhao et al. 2010). ior (Davis 1986, 1989;Koufaris2002; 2007, 2008; Menchen-Trevino and Har- Lu et al. 2003). It is also confirmed gittai 2011). The technology-based ac- 6.5 Opportunity in the context of technology (Ferro tivities studies, rather than the acces- et al. 2011; Ktoridou and Eteokleous- sibility ones, highlight the significant The opportunity factor includes both ac- Grigoriou 2011;Sykesetal.2009). The variances across different demographic cessibility and the opportunity to use behavioral intention to use technology is groups (Bennett and Maton 2010). They technologies to perform daily activities. influenced by many variables, such as de- show that some common activities are Accessibility relates to the level of access mographic characteristics (Li and Ranieri indeed engaged in frequently by young to technology. Other opportunities such 2010; Ching et al. 2005;Ferroetal.2011; people (Bennett and Maton 2010;Jones as faster Internet connections, infrastruc- Hargittai 2010;Hoseinetal.2010;Tømte and Healing 2010). Hence, type of ac- ture (Stern et al. 2009) and technologi- and Hatlevik 2011; Volman et al. 2005; tivity is considered a mediating factor cal support from others (Goode 2010)are Hargittai and Litt 2011; Gudmundsdot- for digital fluency. Many studies use fre- also important. Differences in opportu- tir 2010), organizational factors (Li and quency and type of technology to cre- nities to participate in creative fluency- Ranieri 2010;Barronetal.2010;Hosein ate a typology. This allows the genera- building activities were tied to home ac- et al. 2010; Goode 2010), psychological tion of distinct types of user profiles and cess to tools, size of the non-home access factors (Hargittai and Litt 2011;Jungetal. user groups (Tømte and Hatlevik 2011; network and use of broader resources 2010; Malliari et al. 2011), and social in- Valtonen et al. 2010; Grimley and Allan (Barron et al. 2010). The analysis of orga- fluences (Cotten et al. 2011; Goode 2010; 2010). In summary, the use of technol- nizational factors and demographic char- Gudmundsdottir 2010; Kaare et al. 2007; ogy is positively associated with digital acteristics in the previous sections indi- Thornham and McFarlane 2011; Zhao fluency with technology-based activity as cate their impact on one’s opportunity to et al. 2010). the mediating factor. use technology. Studies show that owning a computer, or having access to a com- 6.7 Use of Technology 6.9 Conceptual Model puter or the Internet at home does not af- fect one’s fluency in using technology (Li The research literature shows that experi- Our analysis of the literature illustrates a and Ranieri 2010; Ching et al. 2005). On ence and frequency of technology use are complicated picture. However, we think the other hand, Brown and Czerniewicz significantly related to one’s digital flu- it allows us to suggest a tentative concep- (2010, pp. 363–364) label young people ency for overall technology use (Li and tual model for digital fluency as shown that have no opportunity or accessibility Ranieri 2010), generic use (Malliari et al. in Fig. 1. An additional relationship is

Business & Information Systems Engineering BISE – STATE OF THE ART

postulated to indicate that digital flu- there is a big disparity between digital na- should be aware of their policy on using ency influences technology use. This pro- tives (who are assumed to be inherently new technologies, especially social net- duces a reciprocal relationship between fluent in IT) and digital immigrants in working tools. A report from software se- technology use and digital fluency. This their use of technology (Prensky 2001b) curity company Clearswift (2011)found dynamism distinguishes digital fluency is false. Rather, there is a continuum be- that 19 % of companies are blocking from general IT traits such as com- tween the two groups and this continuum employee access to sites at puter self-efficacy and personal innova- is best conceptualized as “digital fluency”. work.However,regardlessoftheirpref- tiveness with IT (PIIT) (Agarwal and Also, it is too simplistic to reduce ‘dig- erences over social networking, employ- Prasad 1998). Several studies suggest that ital nativity’ or digital fluency solely to ees highly value freedom and flexibility improvement in digital fluency increases age and accessibility factors; besides these in their work. Moreover, our systematic self-efficacy (Ktoridou and Eteokleous- factors there are psychological, organi- review of the literature shows that digi- Grigoriou 2011)andInternetuse(Ferro zational and social factors that influence tal natives use networking tools more fre- et al. 2011). Therefore, the use of tech- digital fluency. quently. Therefore, companies may need nology is influenced by: (1) opportunity The model of digital fluency that we to rethink their policies about technology – contextual constraints relating to a be- have proposed thus contributes to IS re- useatworkiftheywanttohireandretain havior; (2) intention – the willingness or search in the following ways. First, it sug- digital natives. need to perform an action; and (3) ability gests that IS researchers who are conduct- Second, companies might benefit from (which means digital fluency in our con- ing research on technology adoption, dif- digital natives’ technology skills. Research text) – to have the skills and capabilities fusion, information systems implemen- shows that digital natives are more profi- required to complete the task (Hughes tation and resistance need to be aware cient at incorporating new technology in 2007). Digital natives and digital immi- of the differences between digital natives their personal and professional lives than grants are different in their age and ac- and digital immigrants. Given that all our previous generations, and they bring new cessibility by definition, hence the age previous empirical data in the past has ways of working to the workplace (John- and accessibility contribute to part of the been obtained from digital immigrants, son Controls Research 2011). Addition- demographic and opportunity factors in our models of technology adoption and ally, the younger generation are said to fa- this model. The mixed results of exist- resistance will need to be changed to take vor community building and friendly rit- ing research on digital fluency can be ac- account of the new generation of digi- uals over personal spirituality (Howe and counted for by other variables derived tal natives and their digital fluency. This Strauss 2007). Hence, the way to moti- from the literature. One variation to the couldbedonebyincludingdigitalflu- vate the current generation may be dif- proposed model is to have opportunity ency as a control variable in technology ferent from the previous one. For exam- as a moderator of the intention to use adoption studies. ple, team building, collaboration and fre- – use of technology linkage rather than Second, the model suggests that all IS quent feedback may be their preferred as a direct antecedent of use of technol- studies that are in some way concerned ways for accomplishing tasks, for both ogy. In summary, the differences in op- with users and/or stakeholders need to work and study. In addition, to improve portunity or behavioral intention to use take account of digital fluency. Not all employees’ digital fluency, management IT between digital natives and digital im- users are the same with regard to their could look at the factors described in migrants are the major factors that lead digital fluency. our conceptual model, such as providing to the differences in digital fluency. Third, the model shows that digital flu- training, giving home access to computer ency is dynamic and can change over or the Internet, and/or coaching by peers time. The reciprocal relationship between etc. actual use and digital fluency implies Third, our conceptual model might 7 Discussion there is a potential virtuous circle to im- help organizations to consider how best prove one’s digital fluency. Alternatively, to improve the digital literacy of their em- Given the recent interest in digital na- this could also imply a vicious circle, ployees. Our model identifies the most tives and digital immigrants in informa- which deepens the digital divide. A vi- important factors that should be consid- tion systems (Vodanovich et al. 2010)and cious circle was found in the 2004 Fresh- ered in any digital literacy improvement other disciplines, this paper has suggested man Survey, where the digital divide was effort. that there is a continuum rather than a actually widening for African American rigid dichotomy between digital natives students in the USA (Farrell 2005). 7.3 Limitations and digital immigrants, and this contin- uum is best conceptualized as digital flu- 7.2 Practical Implications Several limitations are associated with ency. Based on a review of the state-of- our paper. First, the topic is relatively art literature on the topic from multi- Two leading international companies new and hence there is a limited amount ple disciplines, we have proposed a ten- have approached us expressing their in- of literature on this topic. Second, we tative conceptual model of digital fluency terest in understanding this new genera- limited our literature search to peer re- that outlines factors that have a direct and tion of employees. Management wished viewed articles only, which means that indirect impact on digital fluency. to uncover if changes should be made we may have missed relevant articles in to the workplace to accommodate digital practitioner magazines and other out- 7.1 Research Contributions natives. This has now become a common lets. Third, the process of paper selec- question in the industry. tion could have been influenced by the Our review of the literature has shown There are three important practical im- quality of the abstract, title and keywords that the underlying assumption that plications of our study. First, companies quality in the databases. If the key words

Business & Information Systems Engineering BISE – STATE OF THE ART

we used do not appear in these sections Acknowledgements of the articles, then they would not have Abstract We would like to thank the editor, asso- been included as part of our evidence Qian (Emily) Wang, Michael D. Myers, base. Although we believe our model is ciate editor and reviewers for their help- David Sundaram fairly comprehensive, there is a possibility ful and constructive comments on this that we missed some articles which em- article. This article is a significantly re- Digital Natives and Digital phasized some factors more than others. vised version of a paper submitted to Immigrants In addition, including both digital natives the European Conference on Informa- and digital fluency in the search criteria tion Systems 2012 and published in its Towards a Model of Digital Fluency narrows our search result. proceedings (Wang et al. 2012). The article looks at the differences be- 7.4 Future Directions tween “digital natives” and “digital im- References migrants.” Digital natives are the new Our research highlights a number of generation of young people born into areas for future research. Agarwal R, Prasad J (1998) A conceptual and the digital age, while “digital immi- operational definition of personal inno- grants” are those who learnt to use First, a significant amount of IS liter- vativeness in the domain of information ature has focused on users’ resistance to technology. Information Systems Research computers at some stage during their new technology. As mentioned, the TAM 9(2):204–215 adult life. Whereas digital natives are Ajzen I (1991) The theory of planned behav- model is widely used in empirical stud- assumed to be inherently technology- ior. Organizational Behavior and Human savvy, digital immigrants are usually ies (Davis 1986;Koufaris2002; Venkatesh Decision Processes 50(2):179–211 2000; Venkatesh and Davis 2000). How- Barron B, Walter S, Martin C, Schatz C (2010) assumed to have some difficulty with Predictors of creative computing participa- ever, the subjects of these earlier studies information technology. tion and profiles of experience in two Sil- The paper suggests that there is a have been digital immigrants. Whether icon valley middle schools. Computers & the same findings will hold when the Education 54(1):178–189 continuum rather than a rigid dich- Bennett S, Maton K (2010) Beyond the “digital subjects are digital natives is open to otomy between digital natives and dig- natives” debate: towards a more nuanced ital immigrants, and this continuum is question. understanding of students’ technology ex- Second, as digital natives start to join periences. Journal of Computer Assisted best conceptualized as digital fluency. Learning 26(5):321–331 Digital fluency is the ability to refor- the workforce, we now have an opportu- Bradley D, Noonan P, Nugent H, Scales B mulate knowledge and produce infor- nity to compare their digital fluency with (2008) Review of Australian higher edu- mation to express oneself creatively their digital immigrant counterparts. cation: final report. Department of Edu- cation, Employment and Workplace Rela- and appropriately in a digital environ- Third, organizational policies with re- tions, Canberra (December) ment. The authors propose a tenta- spect to digital natives’ use of IT need Brown C, Czerniewicz L (2010) Debunking the tive conceptual model of digital flu- to be better formulated. Some companies “digital native”: beyond digital apartheid, towards digital democracy. Journal of Com- ency that outlines factors that have a have banned social networks for reasons puter Assisted Learning 26(5):357–369 direct and indirect impact on digital such as loss of productivity, exposure of Calvani A, Cartelli A, Fini A, Ranieri M (2009) fluency namely, demographic charac- company’s network to viruses, or corpo- Models and instruments for assessing dig- ital competence at school. Journal of e- teristics, organizational factors, psycho- rate information leaks. However, reports Learning and Knowledge Society (English logical factors, social influence, oppor- show that digital natives have different Version) 4(3):183–193 tunity, behavioral intention and actual expectations on how to learn, work and Ching C, Basham J, Jang E (2005) The legacy of the digital divide. Urban Education use of digital technologies. pursue careers (Rainie 2006). How firms 40(4):394–411 can help their employees to increase their Clearswift (2011) Work life web 2011. 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Based on a systematic re- sachusetts Institute of Technology view of the literature from multiple dis- Davis FD (1989) Perceived usefulness, per- ceived ease of use, and user acceptance ciplines, we have proposed a conceptual of information technology. MIS Quarterly model that outlines factors that have a di- 3:319–340 rect and indirect impact on digital flu- Dewan S, Riggins FJ (2005) The digital di- vide: current and future research direc- ency, namely demographic characteris- tions. Journal of the Association for Infor- tics, organizational factors, psychologi- mation Systems 6(12):298–337 cal factors, social influence, opportunity, Dimaggio P, Hargittai E (2001) From the “dig- ital divide” to “digital inequality”: study- behavioral intention, and actual use of ing Internet use as penetration increase. digital technologies. Center for Arts and Cultural Policy Studies,

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