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Human Communication Research ISSN 0360-3989

ORIGINAL ARTICLE

A Model and Measure of MobileCommunication Competence

Emil Bakke

School of Media Arts and Studies, Ohio University, Athens, OH 45701, USA

This article deals with two studies that develop a measure and model of mobile commu-nication competence (MCC). The first study examines the dimensionality of the measureby conducting an exploratory factor analysis on 350 students at a large university in themidwestern United States. Results identified six constructs across 24 items: willingness touse, mobile preference, asynchronous communication, communication efficacy, affect, andappropriate communication. The relationships of the constructs were theoretically synthe-sized in a proposed model of MCC. In the second study, the MCC model was submittedto confirmatory factor analysis on 212 university students in order to test the model’stheoretical structure. Results indicate that the MCC model predicts 59% of the variance inmobile phone use.

doi:10.1111/j.1468-2958.2010.01379.x

Mobile communication has experienced unprecedented growth in users and tech-nological advances over the last decade. With the rapid diffusion of mobilecommunication technology, more than 4 billion mobile phone subscribers worldwidehave an opportunity to interpersonally interact beyond the time and space limitationsof traditional media by instantly sharing pictures of memorable events or shiftingthe concept of time as people are ‘‘softening their schedules’’ (Kelly, 2009; Ling &Haddon, 2003). Users enjoy mobile devices for their converged quality, entertain-ment (e.g., music player, instant messaging, and video), and for other gratificationssuch as fashion (Katz & Sugiyama, 2005; Leung & Wei, 2000; Ling, 2004; Wei, 2008).

The growth of mobile communication has prompted scholars to research mobileuses, technical affordances, and how it modifies the concept of public and pri-vate space. For example, the quality and strength of interpersonal relationships areaffirmed by the length of a mobile communication conversation or the frequencyof text messaging (Licoppe, 2003). Research also suggests that constant connectivityhas created tension between mobile communication users and their environmentby having intimate conversations in public places (Ling, 2002). These studies have

Corresponding author: Emil Bakke; e-mail: [email protected]; [email protected]

348 Human Communication Research 36 (2010) 348–371 © 2010 International Communication Association

E. Bakke Mobile Communication Competence

generated valuable descriptions of mobile communication trends and patterns; how-ever, far less is known about the cognitive constructs that motivate and energizeindividuals’ application of mobile communication. Accordingly, mobile commu-nication literature has identified a need to empirically research users’ cognitiveand social processes to comprehend the underlying decision making that takesplace during mobile communication interactions (Ling & Haddon, 2003; Love &Kewley, 2005).

One way of studying cognitive processes is to place mobile communicationwithin the framework and lens of communication competence theory, which positsthe cognitive motivation and knowledge that stimulate interactants’ communicativebehavior (Spitzberg & Cupach, 2002; Wiemann & Backlund, 1980). Literaturesuggests that communication competence is an essential human need required tofulfill interpersonal objectives and achieve physical and psychological satisfaction;moreover, communication competence becomes increasingly important with theintroduction of technology because it alters people’s communication practices(Spitzberg, 2006; Spitzberg & Cupach, 1984).

In order to research competence and its influence on mobile communication,this project builds on communication competence and mobile phone literature viadevelopment of an instrument and model of mobile communication competence(MCC). By conducting two independent studies, this project seeks to establish areliable and valid measure of MCC; furthermore, it tests the structure of a proposedmodel of MCC.

Theoretical framework

According to Spitzberg and Cupach (1984), communication competence is anessential human need required to fulfill interpersonal objectives and achieve physicaland psychological satisfaction. Some scholars view competency from a behavioralperspective with a focus on the skills needed to complete tasks; moreover, a cognitiveapproach studies the processes underlying the event (Wiemann & Backlund, 1980).Therefore, a cognitive view of communication competence analyzes the potential forperformance, while the behavioral approach is concerned with the efficiency of thecommunication.

Communication competence is a matter of degree. It is not an absolutebecause there are multiple levels of appropriate and effective interpersonal situations(Spitzberg, 1988). Interactants perceive a competent communicator to be relaxed,empathetic, supportive, and able to change their communication practice dependingon the interpersonal encounter (Wiemann, 1977). As such, people’s assessment of aninteraction means more to the relationship than the effect of the message (Canary,Cupach, & Serpe, 2001). This is of theoretical importance to mobile communica-tion scholars because interactants have an opportunity for perpetual interpersonalcommunication through mobile communication devices (Katz, 2006).

Human Communication Research 36 (2010) 348–371 © 2010 International Communication Association 349

Mobile Communication Competence E. Bakke

Much of our daily communication takes place through mediated communicationchannels such as the mobile phone. As suggested by Spitzberg and Cupach (1984),communication practices are altered through the introduction of technology, and theimportance of studying communication competence is increased within mediatedcontexts (Spitzberg, 2006). However, interpersonal competence research does notmeasure the full range of competencies that are required for users to effectivelyand appropriately engage in mediated communication, such as mobile devices (e.g.,learning new features and adapting to changing technology).

There is an emerging thread of literature that has identified a need to evaluatethe mediated communication by understanding the cognitive constructs that guideit; understanding interactants’ mediated communication is key to interpreting usesof the technology (Bunz, 2004; Ledbetter, 2009; Livingstone, 2004; Rice & Bunz,2006; Spitzberg, 2006). As with interpersonal communication, increased mediatedcommunication competence creates psychological and physical satisfaction. Canaryand Spitzberg (1993) discovered that lonely users in some cases select media based onthe gratification of combating loneliness. Later studies on mobile communication alsoidentify that mobile communication plays a central role in strengthening the bondamong members in personal and social networks (Johnsen, 2003; Leung & Wei, 2000).

Seeking to develop a method of evaluating mediated communication, Spitzberg(2006) proposed a model of computer-mediated communication competence inwhich motivation is the initial step in energizing a knowledge search and applicationof skills. Although mobile communication research can benefit from theory andmethods that evaluate mobile interactions, there are marked differences betweenmobile and computer-mediated communication (CMC). For example, there is alarger network of mobile phone users in the world than computer and landlinetelephone users combined; it leads to a larger, diverse network of users who operatewithin cultural norms and practices learned from a variety of influences (Campbell &Russo, 2003; International Telecommunication Union, 2005; Portio Research, 2009).People do not share a dichotomous view of mobile phones’ positive or negativeattributes; personal and cultural differences shape people’s mobile communicationuses and attitudes (Campbell, 2007; Katz & Aakhus, 2002). Furthermore, mobileinteractants have a different level of communicative expectations because theypresume that other mobile users are within close proximity of their device atall times. Consequently, interactants expect immediate and timed responses (Ito,2005; Julsrud, 2005; Schejter & Cohen, 2002). Finally, mobile devices are a privatemedium that frequently invade public space; as such, they add additional levels ofcommunication and negotiation between the mobile user and those sharing physicalpresence, resulting in a struggle to navigate private conversations in public spaces(Katz, 2006; Ling, 2004; Licoppe & Heurtin, 2001). These findings illustrate some ofthe differences between CMC and mobile communication, and while a vast body ofresearch has documented mobile communication uses in social and private contexts,mobile communication research has not provided a model that guides mobile



communication. This study extends mobile communication research by developingand validating a measure and model of MCC.

The items in the measure are conceptually derived from Spitzberg’s proposedmodel of CMC competence (2006). Spitzberg’s model of CMC competence isskillfully argued from a theoretical perspective, though it has proven to be a challengeto implement methodologically due to its complexity of 15 constructs. For example,Bunz (2003) tested, evaluated, and shortened the scale to eight constructs based onexploratory factor analysis. Rice and Bunz (2006) removed a construct from the scalebecause of low reliability. Although the current project builds on Spitzberg’s work, itseeks to develop a fundamentally new scale and model of mobile competence via twostudies that are independent from earlier iterations.

Study 1

The purpose of Study 1 is to develop a reliable and valid measure of MCC. Theconstruct of the measure will be synthesized in a theoretical model. Furthermore,the scale will be tested for validity by evaluating it with common mobile communi-cation practices: Microcoordination describes how mobile communication is usedin organizing activities and managing schedules (Ling & Yttri, 2002); hypercoor-dination suggests that individuals manage several conversations at the same time,which requires maintaining and positioning their Self to others (Ling, 2002). Usingtheir perceptions and uses (PU) of mobile phones scale, Campbell and Russo (2003)discovered that members within personal communication networks shared feelingsof comfort with the technology. Members also agreed on uses such as microcoor-dination and hypercoordination. Because these findings are theoretically relevant tointeractants’ MCC, the MCC construct validity will be established by evaluating itsrelationship with Campbell and Russo’s scale.

MethodInstrument developmentThe items in the MCC scale are adapted from Spitzberg’s proposed CMC CompetenceMeasure version 4 (2006). Items are measured along a 5-point Likert-type responseformat ranging from 1 (Not at all true of me) to 5 (Very true of me). The followingprocess was employed to develop the MCC measure.

Wording from the original 77-item CMC Competence scale such as ‘‘CMC’’ and‘‘computer’’ was replaced with ‘‘mobile phone.’’ For example, the question ‘‘I enjoycommunicating using computer media’’ was changed to ‘‘I enjoy communicatingusing a mobile phone.’’ Though several mobile communication devices have featuresbeyond that of a ‘‘phone,’’ it was theorized that most participants would associatemobile devices such as a Blackberry or the iPhone as a ‘‘mobile phone’’ device. Itemsreferring to technology in general, such as ‘‘Having to learn new technologies makesme very anxious’’ were modified to ‘‘mobile phone features’’ and thus read ‘‘Havingto learn new mobile phone features makes me very anxious.’’ Spitzberg’s proposed



‘‘media factors’’ construct required a large number of changes. The purpose of themedia factor items was to better understand the participants’ media choice. Becausethis study investigates mobile communication devices, all items were modified from‘‘medium’’ to ‘‘feature.’’ The following is an example of this change: ‘‘I choose whichmedium to send some messages by (i.e., CMC, mail, phone, or face-to-face [FtF])’’was changed to ‘‘I choose which feature to use (i.e., text, picture message, or voice).’’Finally, items that had weak theoretical association with mobile communication (e.g.,‘‘I make friends easily’’) were removed.

After modifying each item, the reworded items were pilot tested with a smallsample for final assessment of the wording. Pilot testing included an evaluationand review of the measure’s wording and readability by 10 student volunteers in anintroductory interpersonal communication class. Participants wrote feedback directlyon the measure. Comments were incorporated into the finalized MCC instrumentbefore it was distributed to participants for data collection. The final MCC instrumentcontained 71 items.1

Campbell and Russo’s (2003) PU of mobile phones scale was utilized to test thevalidity of the MCC scale’s factors by investigating its construct validity. The PUis a measure of individual attitude and behavior of several mobile communicationuses and experiences. Not all of these factors are theoretically related to MCC (e.g.,attitudes about public mobile phone use); the following three constructs were usedto investigate MCC’s construct validity: (a) Microcoordination (α = .63, M = 3.36,SD = .56), ‘‘A good reason for owning a mobile phone is to make plans with others’’;(b) Hypercoordination (α = .78, M = 4.57, SD = .61), ‘‘I use my mobile phone to‘catch up’ with friends or relatives’’; and (c) Comfort with technology and service(α = .73, M = 3.36, SD = .69), ‘‘I find the buttons on my phone difficult to use’’and ‘‘I clearly understand all of the details of my calling plan.’’ All of the itemswere measured on a 5-point Likert-type response format ranging from 1 (Stronglydisagree) to 5 (Strongly agree).

Participants and procedureThe study’s sample was composed of 350 students enrolled in a large university in themidwestern United States, and thus contained a sufficient size for scale developmentand factor analysis of the 71-item MCC scale (DeVellis, 2003, p.137). In total, 34% ofthe participants (n = 118) were male, while 64% (n = 224) were female. Participants’age ranged between 18 and 46 years, with a mean of 21.22 (SD = 3.51). All of theparticipants owned a mobile phone, ranging from less than a year to 15 years witha mean of 5.46 years (SD = 2.08). The mean cost of mobile telephone handsets was$155.92 (SD = 107.83) with a reported average monthly mobile phone bill of $71.49(SD = 43.71).

To test reliability, participants were asked to complete a questionnaire with the71-item MCC scale. The procedure was repeated after a 2-month period to ensurethat the participants did not remember prior responses. Self-assigned identificationnumbers were used to match the test–retest samples. Twenty-five participants



completed the test–retest. In total, 48% of the participants (n = 12) were male, while52% (n = 13) were female. Of these, three participants did not complete the surveyso 22 samples were used for the test–retest.

AnalysisAs one of the most frequently used data reduction techniques in interpersonalcommunication (Poole, McPhee, & Canary, 2002), a principal axis factor analysiswith varimax rotation was used to examine the factor structure of the MCCinstrument. The Bartlet test of sphericity suggested that the data met assumptionsnecessary for factor analysis, χ2 = 12655.67 (2,485), p < .001. To determine thestructure of the scale, parallel analysis was used to compute a cutoff value for theeigenvalue in simulated data; furthermore, a visual assessment of the scree-plotdetermined the number of factors to be extracted. The 60/40 criteria (e.g., an itemwas dropped if it failed to produce at least a .60 loading on the primary factor orhad a secondary loading above .40) were used to retain factor loadings to ensuresuitable coefficient values (McCroskey & Young, 1979; see extended discussion offactor loadings in Spector, 1992).

To assess the reliability of the MCC scale, the instrument was retested with thesame sample to obtain correlations between the constructs of the two identicallyadministered tests. According to Carmines and Zeller (1979), the retest method is‘‘one of the easiest ways to estimate reliability of empirical measurements’’ (p. 37).Moreover, it ‘‘represents an intuitively appealing procedure by which to assessreliability’’ (p. 39). Finally, the scale’s construct validity was assessed by investigatingthe relationship between factors on the MCC scale with Campbell and Russo’s PU ofmobile phones scale (2003).

ResultsExploratory factor analysis of the MCC scaleBased on the criteria given, the factor analysis yielded six constructs: asynchronouscommunication, comfort with technology, mobile preference, appropriate commu-nication, communication efficacy, and communication affect. Below is a descriptionof the factors with sample items; additionally, the rotated component matrix ofconstructs with respective scale items is listed in Table 1.

The first factor contained items that measure asynchronous communicationcompetencies (e.g., ‘‘I make sure my objectives are emphasized in my mobile phonetext messages’’). Individuals who score high on this factor display certainty andconfidence in text message interactions. They also craft articulate, vivid, yet assertivetext messages.

The second factor contained items that evaluate users’ comfort with mobiletechnology (e.g., ‘‘I know I can learn to use new mobile phone technologies whenthey come out’’). Individuals who score high on this factor are comfortable using allthe features on their mobile device, quickly learn new mobile features, and believethey can quickly adapt to technological change.



Table 1 Mobile Communication Competence Scale: Rotated Component Matrixa

Component

Items 1 2 3 4 5 6

Asynchronous communicationDisplay certainty in text message .745Confident style .699 .203Adapt text message to receiver .695I can emphasize my objective .684Articulate and vivid text message .669 .217Assertive writing style .664Skilled at prioritizing text .636 .206 .211Show empathy in text messages .605Skilled at timing text response .599 .249Use humor in text .584Use expressive symbols in text .459 .239

Comfort with technologyQuickly learn new features .723Not skilled learning features .689Confident learn future MP technologies .677Knowledgeable MP communication .654 .227Can learn new technology .651Familiar with features .227 .635Capable of using features .618Nervous to learn features .312 .576Changes in tech frustrating .564Adapt messages to feature .534Manage interactions skillfully .370 .225 .276 .305Nervous about using the mobile phone .293 .249Know when to close a topic of conversation .281 .202 .202Nervous communicating with mobile .231Will not use feature if not user friendly .201

Mobile preferenceUse MP constantly .811Heavy mobile phone (MP) user .233 .739Rely on MP .725Tend to use MP .725 .230Enjoy MP communication .237 .612 .401Most efficient using MP .558Get a lot accomplished using MP .520 .243Can’t go a week without MP .509MP more productive than FtF .245 .505Motivated to use MP .252 .496 .396Look forward to using MP .208 .234 .463 .388 .226MP is timesaver .447 .210 .208



Table 1 Continued

Component

Items 1 2 3 4 5 6

EfficacyAchieve goals in interaction .728Interactions are effective .684Effective conversations .210 .616 .205Get what I want from interaction .569 .126Comments are accurate .562 .261Get ideas across clearly .538 .338Understood when interacting .444 .326Messages are rarely misunderstood .443Know how to say .298 .379Skillful at revealing composure .331 .251 .362 .253No trouble expressing opinion .255 .238 .215 .317Always know what to say .279

AffectPleased with interactions .331 .673Feel good about conversations .301 .649Enjoy interacting .607 .212Get people to like me .251 .514People would like to know me .236 .494People enjoy my company .261 .493Satisfied with mobile com .419 .475Show concern for others .335 .271

AppropriateInterchange of ideas .648Info in message .210 .640Intimacy of message .604Quickly you need response .204 .585Speed of message .213 .536Access other’s have to feature .494Avoid offensive communication .208 .465Careful comments .461Pay attention to appropriate .324Never say offensive things .216 .288

Note: Only coefficient values >.20 are listed. Extraction method: principal axis factoring.Rotation method: varimax with Kaiser normalization.aRotation converged in six iterations.

The third factor contained items evaluating the degree of preference users havefor mobile communication (e.g., ‘‘I use a mobile phone as means of communicationalmost constantly’’). Individuals who score high on this construct rely and depend



on daily mobile communication, believe that mobile devices are timesavers, and theyare motivated and look forward to using mobile devices to communicate with others.

The fourth factor contained items evaluating whether users engage in effectivemobile communication (e.g., ‘‘My mobile interactions are effective in accomplishingwhat I set out to accomplish’’). Individuals who score high on this constructconsistently achieve their goals in their mobile communication; moreover, theyevaluate their mobile communication conversations as effective.

The fifth factor contained items evaluating users’ mobile communication affect(e.g., ‘‘I enjoy my mobile phone interactions with others’’). Individuals who scorehigh on this construct are satisfied with their mobile communication and enjoy theirmobile phone interactions.

Finally, the sixth factor contained items evaluating users’ ability to select a mobiledevice feature that is an appropriate channel for the content and purpose of thecommunication (e.g., ‘‘I choose which mobile phone feature [i.e., voice, text, andpicture message, etc.] to communicate with based on the extent to which I need toget some ‘back and forth,’ ‘give and take,’ and interchange of ideas’’). Individualswho score high on this factor select a mobile feature that, in their judgment, offersthe greatest chance for a successful outcome of a mobile communication.

Cronbach’s alpha was conducted to investigate the internal consistency of thefactors in the scale. After removing four items to increase internal reliability, thefinal six factors had internal reliability above .75, thus exceeding the standard foracceptable reliability in widely used scales (Babbie, 2007; Carmines & Zeller, 1979;Nunnally, 1978). Two of the items were removed from the asynchronous messagingfactor (‘‘I am skilled at prioritizing [triaging] my text messages,’’ and ‘‘I take time tomake sure my text messages to others are uniquely adapted to the particular receiverI’m sending it to’’); two items were also removed from the comfort with technologyfactor (‘‘I don’t feel very skilled in learning and using mobile phone features,’’ and ‘‘Iam very knowledgeable about how to communicate with mobile phones’’). The MCCscale’s six factors with descriptive statistics are listed in Table 2 (see the Appendix forfinal 24-item instrument).

Table 2 Mobile Communication Competence Subscales

α M SD N

Asynchronous messaging .86 3.42 .89 348Mobile com preference .89 4.07 .94 350Comfort with technology .85 4.17 .72 350Appropriate .75 4.16 .82 348Efficacy .80 3.93 .60 350Affect .87 4.27 .64 350



Table 3 Test–Retest Correlations Between Mobile Communication Competence (MCC)Subscales

r N

Asynchronous messaging .63 22Mobile preference .87 22Comfort with technology .93 22Appropriate .84 21Efficacy .65 22Affect .65 21

Note: Correlations were significant at the p < .05 level.

Reliability and validity of the MCC scaleA measure of reliability was the consistency and correlation between the six MCCconstructs of two identically administered MCC tests. The results of the correlationanalysis shown in Table 3 indicate that all the subscale correlations between thetest–retest were statistically significant and were greater or equal to .63.

In order to test the MCC scale’s construct validity, correlation coefficients werecomputed to investigate the relationship between the MCC subscales and the PUscale (see descriptive statistics in Table 4). Results of the analysis indicate thatall MCC subscales correlated with PU constructs. In general, there were slight tofair degrees of relationships between all the constructs in the MCC scale and thehypercoordination factor. The highest degree of relationship was found betweenthe comfort with technology subscales as well as hypercoordination and mobilecommunication preference. The relationship between the six MCC subscales andCampbell and Russo’s (2003) PU of mobile phones’ subscales impacts the constructvalidity of the MCC scale because constructs that were conceptually known to bedirectly related to constructs in the MCC scale were significant.

Table 4 Perceptions and Uses of Mobile Phones: Correlations Between MobileCommunication Competence (MCC)

Mobile Communication Competence

CT MP AM AP EF AF M SD

Microcoordination .26∗ .45∗ .10 .32∗ .30∗ .36∗ 4.33 .56Hypercoordination .33∗ .53∗ .14∗ .25∗ .25∗ .43∗ 4.57 .61Comfort with technology and service .47∗ .13∗ .33∗ .12∗ .16∗ .15∗ 3.36 .69

Note: Correlations were significant at the p < .05 level. CT = comfort with technology; MP =mobile preference; AM = asynchronous messaging; AP = appropriate; EF = efficacy; AF =affect.



Discussion

The purpose of this study was to develop and validate a scale that may aid inquiryregarding cognitive processes and competencies involved in mobile communication.Factor analysis produced a six-factor measure. The 24-item MCC scale demonstratedreliability and validity. This section provides a synthesis of the study’s results;specifically, the importance of the six competencies measured in the MCC scale isgrounded in current mobile communication literature. Finally, as shown in Figure 1,the relationships of the constructs are theoretically synthesized in a proposed modelof MCC.

Based on the results of this study, I argue that MCC is a series of cognitiveconstructs in which (a) users’ mobile preference, comfort with technology, and levelof asynchronous communication competency acts as a motivating and energizing

Mobile Communication

Interaction

Application

Motivation Motivation

Context

Figure 1 Model of mobile communication competence.



factor that (b) prompts the application of an appropriate, effective, or affectivemobile communication. Figure 1 illustrates the relationship among the six factors ina proposed model of MCC in terms of motivation and application.

Motivation represents a synergy between the ‘‘mobile preference,’’ ‘‘comfort withtechnology,’’ and ‘‘asynchronous communication’’ whereby users’ self-monitoringof mobile communication creates a feedback loop between the three constructs.Individuals who score high on any of the motivation constructs are energized to learnnew mobile technologies, have a preference for mobile use, or engage in frequentasynchronous messaging. Conversely, low competence in any of these constructs willbecome an avoidance motive and thus decrease mobile communication. This notionis also supported in literature in that individual motivation and preference for usingmobile devices guide everyday uses (Leung, 2007; Wei & Lo, 2006).

Individuals who prefer mobile communication display commitment and showreciprocity to individuals in their social network by giving out their mobile number,answering calls, sending text messages, and initiating mobile phone conversations(Licoppe & Heurtin, 2001). Specifically, there are two trends among those who prefermobile communication: They tend to engage in lengthy mobile phone conversations,sometimes even ritualized, which takes time out of a person’s day, and they employshort and frequent conversation gestures, such as text messages, to affirm thestrength of interpersonal relationships (Licoppe, 2003). The integration among theseconstructs is supported in that an individual’s mobile phone preference also act as amotivator to learn new mobile communication technologies (Leung, 2007).

Research also suggests that individual level of comfort with technology is an indi-cation of mobile communication competency. Early adopters of mobile technologyare less concerned about features and applications, instead acquiring a mobile devicefor safety and coordination purposes (Ling & Haddon, 2003). Although researchsuggests that users’ technological competencies increase with time and influence thetypes of mobile communication, low levels of comfort with technology may create astate of technological apprehension guiding users to refrain from adopting and usingtechnology to capacity (Ling, 2004, Scott & Timmermann, 2005).

Asynchronous communication is the last motivational component. Text messag-ing plays a central role in mobile communication. In a Norwegian study, teenagersfound SMS to be the preferred form of interaction compared to e-mail, instantmessenger, mobile voice, or landline telephone calls (Ling, 2004), even claiming thatmobile communication is equated to text messaging. Furthermore, as literature sug-gests, common mobile communicative activities such as hypercoordination—whereusers engage in several simultaneous communications—are in part afforded by thepresence of asynchronous messaging (Ling, 2004). Recent studies also indicate thatusers’ asynchronous communication is tightly related to more generalized communi-cation patterns. Those who are unwilling to communicate FtF communicate less withmobile phone text messages than those who are more involved in FtF communication(Leung, 2007). Considering the importance of asynchronous communication, thisconstruct is one of the three competencies theorized to be a central and initiating



motivator for individual mobile communication. As proposed in the model of MCC,motivation is followed by application of the mobile communication.

Application is measured along the lines of effective, affective, and appropriatecommunication. Ultimately, users’ evaluation of previous mobile communicationswill influence the application of purposeful communication. When interactants havea feeling of social and personal accomplishment after using a mobile device, they aremore likely to repeat mobile communication and to be more motivated to do so.Conversely, if users are not able reach their goals with a mobile device, they are lesslikely to repeat that use of mobile communication.

Efficacy is a salient reason for mobile communication as users frequently coor-dinate activities and manage their schedules in both personal and business contexts;about 30% use their mobile device to coordinate, plan, and rearrange agreements(Julsrud, 2005; Ling, 2004; Ling & Haddon, 2003). Efficiency is also a result from asyn-chronous messaging; although it takes time typing a text message, mobile phone usersconsider it efficient because they do not waste time with verbal banter (Ling, 2004).

Affective and social communication is a significant gratification for using themobile phone (Leung & Wei, 2000). Research suggests that mobile communication isnot only purposeful (e.g., coordinate activities), but that it frequently reinforces andsupports social ties among members by organizing the topic of talk in smaller groups.Furthermore, those who frequently meet FtF also communicate more with theirmobile device (Ishii, 2006; Taylor, 2005). Ling’s concept of ‘‘grooming messages’’may also be considered a sign of affective mobile communication (2004). These textsare considered ‘‘gifts’’ to the recipient (e.g., ‘‘great party last week,’’ or ‘‘it was so niceto see you again’’) and constitute 17% of all text messages sent.

Finally, appropriate competency indicates that users are likely to use a specificfeature based on the quantity of information communicated and the degree ofreciprocity the interactions require, such as interchange of ideas, communicationliveliness, and intimacy. Such competency guides daily and commonplace mobilecommunicative activities such as microcoordination where the mobile device is usedto arrange the day as it takes place (Ling & Yttri, 2002). There are also examples ofhow culture guides users’ management of communication features afforded by theirmobile device. In Japan, mobile users do not talk on the phone in public. Ito (2005)found that, in some examples, a Japanese teen’s text messaging during the day wouldeventually lead to a voice call later at night. It is one example of how individualappropriate communication competency guides the feature that is appropriate forthe communication in given context.

Study 2

The study thus far has established a 24-item MCC scale, which demonstratedreliability and validity. Furthermore, the six-factor model was synthesized in aproposed, theoretically derived, model of MCC.



Study 2 extends these results and theory by submitting the model of communica-tion competence to confirmatory factor analysis (CFA), a technique that holisticallyand deductively tests data against a theoretical factor structure specified a priori bythe researcher. The CFA and structural model specification is grounded in the abovediscussion and proposed model of MCC (Figure 1): First, the three exogenous moti-vating constructs (mobile preference, comfort with technology, and asynchronouscommunication) are hypothesized to be positive predictors of users’ applicationof mobile communication (appropriate, effective, and affective communication);second, the endogenous constructs that measure users’ application of MCC arehypothesized to be positive predictors of individual mobile phone use (see Figure 2for complete proposed model with hypothesized relationships).

MethodParticipantsThe sample was composed of 212 students enrolled in a large public U.S. university.In total, 36% of the participants (n = 76) were male, while 64% (n = 136) werefemale. Participants’ age ranged between 18 and 45 years, with a mean of 19.82(SD = 2.82). All of the participants owned a mobile phone ranging from less than1 year to 15 years with a mean of 4.95 years (SD = 2.08). Participants reported usingtheir mobile phone to make an average of four voice calls in a typical day (M = 4.23,SD = 4.85). Furthermore, participants used their mobile device to send an averageof 63 texts in a typical day (M = 63.42 , SD = 112.050).

+

+

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+

+

Comfort with Mobile Tech

Mobile Preference

Asynchronous Communication

Efficacy

Affect

Appropriate

Use of Mobile Com

+

+

+

+

+

+

+

Figure 2 Hypothesized model of mobile communication competence.

Note: Each path represents a hypothesized relationship among the latent constructs in themobile communication competence model.



Procedure and instrumentParticipants were asked to complete a questionnaire consisting of the 24-item MCCscale derived from Study 1 (see the Appendix). The following reliability coefficientsfor the MCC scale were obtained in this study: .85 for asynchronous communication(M = 3.64, SD = .69), .86 for mobile preference (M = 3.69, SD = .82), .86 forcomfort with technology (M = 4.12, SD = .68), .70 for appropriate communication(M = 4.25, SD = .66), .78 for communication efficacy (M = 3.81, SD = .57), and.91 for communication affect (M = 3.87, SD = .69).

Participants were also asked to complete questions regarding their advancedmobile uses across a range of concepts (α = .91, M = 2.25, SD = 1.07; e.g., ‘‘Do youuse your mobile phone to access information online?’’ and ‘‘Do you personalize yourphone by changing the ringtone or wallpaper setting?’’). Participants were also askedto complete questions regarding their general text and voice mobile uses amongfriends and family (α = .78, M = 4.02, SD = .75; e.g., ‘‘Do you use your mobilephone to send/receive text messages with family members?’’ and ‘‘Do you use yourmobile phone to communicate in work or professional situations?’’). All of the itemsin the two mobile phone use scales employed a 5-point Likert-type response formatranging from 5 (All the time) to 1 (Not at all).

Data analysisThe overall structure of the MCC scale was tested via CFA and structural equationmodeling (SEM) with maximum likelihood (ML) estimation. Two primary advan-tages of SEM are the holistic assessment of an a priori specified model (Figure 2)and the ability to correct error variance, thus to identify parameters of interest moreaccurately.

All CFA procedures were conducted using LISREL 8.80. The model fit wasassessed using four frequently reported fit indices: (a) model chi-square, (b) the rootmean square error of approximation (RMSEA), (c) the nonnormed fit index (NNFI),and (d) the comparative fit index (CFI) (Kline, 2005). Model chi-square is a basicstatistic-assessing model fit, with good fit indicated by nonsignificant chi-squarevalues. Scholars suggest that the model fit is generally considered acceptable if CFIand NNFI values are above .90 and the RMSEA statistic does not exceed .08 (Kline,2005; MacCallum, Browne, & Sugawara, 1996).

The hypothesized measurement model, as shown in Figure 2, included seven latentconstructs: (a) mobile preference, (b) comfort with technology, (c) asynchronouscommunication, (d) affect, (e) efficacy, (f) appropriate communication, and(g) mobile phone use. All of the 24 items in the MCC scale were treated as sin-gle manifest indicators in order to theoretically evaluate each item as part of creatinga robust and parsimonious scale (Kline, 2005). Moreover, three indicators identifiedthe mobile use construct: self-reported daily mobile voice calls, as well as two parcelsof advanced and generalized mobile uses (‘‘aggregate-level [indicators] comprised ofthe sum (or average) of two or more items, responses, or behaviors’’; Little, Cun-ningham, Shahar, & Widaman, 2002, p. 152). Given the unidimensional nature of



these indicators, each parcel contained all items for the respective measure (e.g., thefirst parcel contained all the items in advanced mobile uses, while the second parcelcontained all items in the general mobile uses).

The data set contained a small amount of missing data among the continuousvariables; the missing values were imputed using the missing values procedure forexpectation-maximization (EM) imputation in SPSS (Kline, 2005).

ResultsConsistent with two-step modeling procedures, outlined by Kline (2005), a CFAof the measurement model was conducted to assess the relationships among indi-cators and their respective latent constructs prior to testing the structural model.The confirmatory model produced an acceptable model fit, χ2(303) = 556.646,RMSEA = .058[90% CI = .049:.066], NNFI = .97, CFI = .97.

After establishing acceptable fit for the measurement model, the hypothesizedregression paths were tested in a structural equation model (Figure 3). The ini-tial structural model demonstrated acceptable fit, χ2(309) = 583.010, RMSEA =.059[90% CI = .049:.066], NNFI = .97, CFI = .97, but also revealed the presence ofnonsignificant regression paths. The nonsignificant paths were removed throughiterations using parameter z-scores as the removal criterion (Kline, 2005). Thistrimmed model also demonstrated acceptable fit, χ2(311) = 584.760, RMSEA =.059[90% CI = .049:.066], NNFI = .97, CFI = .97, with a chi-square difference testindicating a nonsignificant decline in fit relative to the initial structural model,�χ2(2) = 1.75, p > .05.

Comfort with Mobile Tech

R2 .56

R2 .20

.37**

.40**

R2 .59

Mobile Preference

Asynchronous Communication

Efficacy

Affect

Appropriate

Use of Mobile Com

R2 .59

.60**

.29**

.32**

.23**

.22*

.30**

.26*

.33*

Figure 3 Structural model of mobile communication competence.

Note: χ2(311) = 584.760, RMSEA = .059[90% CI = .049:.066], NNFI = .97, CFI = .97.



As shown in Table 3, the three exogenous, motivational competency factors weresignificant predictors of individual mobile application. First, 59% of the variancein users’ mobile communication efficacy was predicted by their comfort with tech-nology (B = .57[95% CI = 0.32:0.82], β = 0.37[95% CI = 0.20:0.53], p < .01), asynchronouscommunication (B = .50[95% CI = 0.25:0.75], β = 0.32[95% CI = 0.16:0.48], p < .01), andmobile preference (B = .45[95% CI = 0.19:0.71], β = .29[95% CI = 0.12:0.46], p < .01).

The model also predicted 20% of users’ appropriate mobile communication.The results from the regression paths suggested that appropriate communicationwas positively predicted by asynchronous communication (B = .33[95% CI = 0.11:0.55],β = 0.30[95% CI = 0.15:0.44], p < .01) and mobile preference (B = .25[95% CI = 0.03:0.46],β = .22[95% CI = 0.03:0.41], p < .05). Comfort with technology was not a significantpredictor of affective communication.

Regarding affect, 56% of the variance in users’ mobile communication affectwas predicted by their asynchronous communication (B = .34[95% CI = 0.13:0.56], β =0.23[95% CI = 0.09:0.37], p < .01) and mobile preference ( B = .91[95% CI = 1.49:0.86], β =.60[95% CI = 0.43:0.77], p < .01). Comfort with technology was not a significant predictorof affective communication.

Finally, the three endogenous mobile application competency constructs pre-dicted 59% of mobile communication usage (Table 3). The results from theregression paths suggested that mobile use was positively predicted by efficacy(B = .33[95% CI = 0.07:0.59], β = 0.33[95% CI = 0.09:0.57], p < .01), appropriateness (B =.36[95% CI = 0.04:0.68], β = 0.26[95% CI = 0.03:0.49], p < .05), and mobile communicationaffect (B = .42[95% CI = 0.15:0.69], β = .41[95% CI = 0.14:0.67], p < .01).

Discussion and theoretical synthesisThe study sought to develop a valid, reliable measure of MCC. The relationships of theconstructs were theoretically synthesized in a proposed model of MCC (Figure 1). Thismodel was submitted to CFA in order to test the model’s theoretical structure. Resultssuggest that 59% of the variance in mobile phone use was predicted by the MCC model(Figure 3). CFA confirmed the theorized model in that comfort with technology,mobile preference, and asynchronous communication competencies were exogenousvariables that function as motivation and predictors of users’ application of mobilecommunication. Combined, these variables explained 56% of mobile communicationaffect, 59% of efficacy, and 20% of appropriate mobile communication. Furthermore,affect, efficacy, and appropriate mobile communication positively predicted mobilephone use (59%). Not only the proposed model and measure of MCC aid researchersin identifying specific user processes, but also create an empirical framework forfuture discourse and research.

Based on the results of this study, MCC is a series of cognitive constructs in which(a) users’ mobile preference, comfort with technology, and level of asynchronous com-munication competency act as motivating and energizing factors that (b) prompt theapplication of an appropriate, effective, or affective mobile communication. Figure 1illustrates the relationship among the six factors in terms of motivation and application.



Of the three motivating factors, asynchronous communication positively pre-dicted all three of the mobile application constructs. This is expected becauseasynchronous communication is used in crafting affective messages for maintainingpersonal relationships (Licoppe, 2003), it is commonly used in effective mobilecommunication through coordination of daily activities (Julsrud, 2005; Ling, 2004;Ling & Haddon, 2003), as well as engaging in appropriate mobile communicationby, for example, sending grooming messages (Ling, 2004). This result confirmsthe motivational importance of asynchronous communication. Mobile preferencealso positively predicted all three of the mobile application constructs. The resultssuggest that individuals who prefer mobile communication are motivated to usetheir mobile devices in affective and effective communication. Furthermore, mobilepreference is a positive predictor in individuals’ evaluation of the appropriate featurefor the intended mobile communication. Finally, comfort with mobile technologywas a positive predictor of communication efficacy. Being comfortable with newtechnologies and unafraid of technological changes suggest that the mobile phoneinteractant may be a goal-oriented individual, thus more likely motivated to engagein efficient mobile application. On the contrary, it is not unexpected that individuals’comfort with technology does not predict affect or appropriateness. Current litera-ture suggests that technological competence/skill does not consistently translate tothe quality/amount of communication in new technology media (Ledbetter, 2009;Leung, 2007; Scott & Timmermann, 2005). A likely interpretation of the result isthat users’ mobile preference or asynchronous communication becomes the primarymotivator for affective or appropriate communication.

All of the three mobile application factors (appropriate, effective, and affectivecommunication) were positive predictors of individual use of mobile communication.This finding is supported in literature, which suggests that mobile interactants usetheir mobile device to engage in social communication and maintain personal andintimate relationships (Ishii, 2006; Leung & Wei, 2000; Taylor, 2005); mobile devicesare frequently used in coordinating and organizing personal and work-relatedactivities (Julsrud, 2005; Ling & Yttri, 2002); and competent mobile interactantscarefully choose communication that is appropriate for the recipient and the context(Ito, 2005; Ling, 2004).

The MCC model advances knowledge about the relationship among com-petencies; however, scholars must take the mobile communication context intoconsideration when evaluating individual MCC. Context is a critical considerationwhen studying communication (Spitzberg, 2006) and is equally important to mobilecommunication because mobile interactions do not take place in a vacuum—theyaffect or are affected by contexts.

A mobile communication context is defined here as the environment in which themobile communication takes place. Contexts can be both physical and psychological.Considering the literature, the following four contexts are expected to influenceindividuals’ MCC: communicator, relationship, location, and culture. The commu-nicator affects a mobile phone interaction in several ways, although technical skills



enable communicators to use a variety of mobile phone features, psychologicaltraits, such as communication apprehension, may constrain mobile communication(Bakke, 2009; Love and Kewley, 2005). Relationship is an essential aspect of mobilecommunication because interactants use their phone to create and maintain relation-ships. For example, mobile communication is used differently when communicatingwith a coworker compared to a friend (Ito, 2005; Katz, 2006). The location deter-mines what is said and how it is communicated; interactants gauge the interactionby selecting features and maintaining their Self based on location (Ito, 2005; Ling& Yttri, 2002). Culture represents beliefs, values, and behaviors that are created andshared within a group. All mobile interactions are shaped by or contribute to largersocial forms and patterns. For example, age, gender, and nationality influence howthe medium is used, what is communicated, and where it is socially accepted tointeract (Goggin, 2008).

Conclusions

The current project advances our understanding of mobile communication bycreating a model and measure of MCC. However, it is important to note thatthe study is derived with data from college students in the United States. Thispopulation was selected because an extensive amount of literature suggests that theage group of college students is composed of frequent and heavy users of the mobilecommunication technology (Campbell, 2006; Harrison Interactive, 2008; Wei &Lo, 2006). Because MCC is contextually bound, the model of MCC should not begeneralized beyond the current population and cultural context. Considering thesample, centrality of context, and the theoretical underpinnings of this study, thefollowing three areas emerge as logical, future applications of the MCC measure.

First, it is important to investigate communalities or differences in MCC acrosscultures. At present, mobile communication research has identified unique usesof mobile devices dependent on interactants’ cultural background. For example,interactants, nationality affects interactants, attitudes and perception of mobilephone use (Campbell, 2007; Lemish & Cohen, 2005), age can be a predictor ofwhy interactants adopt the technology (Ling, 2004), while gender can determine thefrequency and motives of mobile communication (Horrigan, 2008; Wei & Lo, 2006).

Secondly, there is a need to learn more about MCC in context of social and personalnetworks. Although we know that members of the same network share feelings ofcomfort with technology (Campbell & Russo, 2003), mobile devices stimulate howpeople negotiate space (Weilenmann, 2001), and are used to create, maintain, andstrengthen personal and social networks (Johnsen, 2003). Little is known about therole of MCC in creating and maintaining social and personal networks.

Finally, more research is required to investigate MCC and mobile phone interac-tions in public and private space. For example, teens struggle with privacy, intimacy,and autonomy regarding friends’ constant mobile phone use and accessibility, even tothe point of feeling threatened when friends receive constant mobile communication



from others (Katz, 2006; Ito, 2005). Users’ competence level may play an importantrole in negotiating these types of interactions.

This study sought to develop a model and measure to explain the processesunderlying mobile communication. This is especially important as other mediaconverge onto mobile communication, and users grow more dependent on thetechnology. Mobile communication smart-phone technologies, such as Apple’siPhone, Blackberry’s Storm, and Palm’s Pre, allow users to move beyond voice andtext communication through the addition of high-speed Internet, television, and videocapabilities. With these additional features, users begin to incorporate their phonesin every aspect of communication. For example, Campbell and Kwak (forthcoming)found that patterns of mobile communication, and user competencies, could belinked to civic and political engagement, further underscoring the significance ofMCC (Gergen, 2008). As such, MCC research may provide valuable new informationabout a communication device people use on a daily basis in order to fulfill a myriadof communicative needs.

Acknowledgments

I am grateful for the invaluable assistance of Dr. Roger Cooper and Dr. AndrewLedbetter during this project. I would also like to thank Dr. Chad Edwards for hishelp during the initial stages of this research.

Notes

1 Contact the author for a copy of the exploratory factor analysis item pool.

Appendix: Mobile communication competence instrument

Mobile phone (cell phone) communicationInstructions: We are interested in how people use mobile phones (cell phones) tocommunicate with others. Indicate the degree to which you agree or disagree witheach statement regarding your use of mobile communication using the followingscale:

1 = Not at all true of me2 = Mostly not true of me3 = Neither true nor untrue of me; Undecided4 = Mostly not true of me5 = Very true of me

1. My mobile phone text messages are written in a confident style.2. I know I can learn to use new mobile phone technologies when they come out.3. I feel good about my mobile phone conversations.



4. I am very familiar with the features on a mobile phone.5. I use an assertive style when writing a text message.6. I choose which mobile phone feature (i.e., voice, text, and picture messages, etc.)

to communicate with based on the extent to which I need to get some ‘‘back andforth,’’ ‘‘give and take,’’ and interchange of ideas.

7. I quickly figure out how to use new features on a mobile phone.8. I enjoy communicating using mobile phones.9. I choose which mobile phone feature (i.e., voice, text, and picture messages, etc.)

to communicate with based on how personal or intimate the information in themessage is.

10. I rely heavily upon my mobile phone for getting me through each day.11. I display a lot of certainty in the way I write my text messages.12. I am confident that I will learn how to use any new features that are due to come

out.13. If I can use a mobile phone for communicating, I tend to.14. I make sure my objectives are emphasized in my mobile phone text messages.15. I consistently achieve my goals in mobile phone interactions.16. I enjoy my mobile phone interactions with others.17. My mobile interactions are effective in accomplishing what I set out to accomplish.18. I feel completely capable of using almost all the features available on a mobile

phone.19. I use a mobile phone as means of communication almost constantly.20. I choose which mobile phone feature (i.e., voice, text, and picture messages, etc.)

to communicate with based on how much information is involved in the messageI need to communicate.

21. I am a heavy user of mobile phone communication.22. I am generally pleased with my mobile phone interactions.23. I am very articulate and vivid in my mobile phone text messages.24. I am effective in my mobile phone conversations with others.

Sum the items in parenthesis for subscale scoring: Asynchronous messaging (1, 5,11, 14, 23); Appropriate (6, 9, 20); Comfort with technology (2, 4, 7, 12, 18); Efficacy(15, 17, 24); Affect (3, 16, 22); Mobile preference (8, 10, 13, 19, 21).

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移动通信能力

【摘要：】

本文报告了两项研究以提出移动通信能力的测量方法和模型。第一个研究通

过对 350位大学生样本的探索性因子分析以研究量表的维度。研究结果确定了

24条项目中的 6个结构成分：使用愿意程度、移动偏好、异步通讯、通讯效率、

情感和交流适当性。这些结构成分的关系在理论上被综合成为移动通信能力的模

型。为测试该模型的理论结构，第二项研究对 212位大学生样本进行验证性因子

分析。结果表明，移动通信能力能够预测手机使用中 59%的变量。

Résumé

Ce manuscrit fait état de deux études qui développent une mesure et un modèle de la

compétence en communication mobile (CCM). La première étude examine la dimensionnalité de

la mesure en conduisant une analyse factorielle exploratoire auprès d’un échantillon de

350 étudiants universitaires. Les résultats ont permis d’identifier six construits à travers

24 éléments : la volonté d’utiliser, la préférence pour la communication mobile, la

communication asynchrone, l’efficacité de la communication, l’affect et la communication

appropriée. Les relations des construits sont synthétisées théoriquement dans un nouveau modèle

de la compétence en communication mobile. Dans une seconde étude, le modèle de la CCM est

soumis à une analyse factorielle confirmatoire à partir d’un échantillon de 212 étudiants

universitaires, afin de tester la structure théorique du modèle. Les résultats indiquent que le

modèle de la CCM prédit 59 % de la variance dans l’usage des téléphones mobiles.

Mots clés : communication mobile, téléphones mobiles, téléphones cellulaires, compétence,

CMO, communication médiée par ordinateur, compétence interpersonnelle, compétence

communicationnelle, aisance avec la technologie

Der Artikel berichtet von zwei Studien, die ein Modell und Messverfahren für Mobilkommunikationskompetenz entwickelt haben. Die erste Studie untersucht die Dimensionalität des Messverfahrens mittels einer explorativen Faktorenanalyse anhand einer Stichprobe von 350 Universitätsstudierenden. Im Ergebnis zeigen sich 6 Konstrukte mit 24 Items: Willen zur Nutzung, Mobile Präferenz, Asynchrone Kommunikation, Kommunikationsselbstwirksamkeit und angemessene Kommunikation. Die Beziehungen der Konstrukte werden theoretisch in einem Modell der Mobilkommunikationskompetenz synthetisiert. In einer zweiten Studie wurde das Modell mittels konfirmativer Faktorenanalyse mit einer Stichprobe von 212 Universitätsstudierenden überprüft, um die theoretische Struktur des Modells zu testen. Die Ergebnisse zeigen, dass das Modell 59% der Varianz der Handynutzung erklärt. Schlüsselbegriffe: Mobilkommunikation, Handy, Kompetenz, computervermittelte Kommunikation, interpersonale Kompetenz, Kommunikationskompetenz, Technologie‐Fluss

3

요약

본 논문은 모바일커뮤니케이션능력 (MCC)의 척도와 모델을 발전시키려는 2 개의 연구에

고나한 것이다. 첫번째 연구는 350 명 대학생을 대상으로 한 탐색적 요소분석을 단행하는

것에 의해 척도의 차원을 연구한 것이다. 결과들은 24 항목에 걸쳐 6 개의 구성을

확인하였는바, 그들은 사용하려는 의지, 모바일 선호도, 비동시적 커뮤니케이션,

커뮤니케이션 효과성, 영향, 그리고 적절한 커뮤니케이션이다. 구성요소들의 관계들은

제안된 모바일커뮤니케이션능력모델에서 이론적으로 합성되었다. 두번째 연구에서는,

MCC 모델이 이 모델의 이론적 구조를 테스트하기 위하여 212 명의 대학생들을 대상으로

한 확인적 요소분석을 위해 사용되었다. 결과들은 MCC 모델은 모바일 전화사용에

있어 59%의 변수들을 예측한다는 것을 보여주었다.

Resumen

Este manuscrito reporta dos estudios que desarrollaron una medida y un modelo de Competencia

de Comunicación Móvil (MCC). El primer estudio examina la dimensión de la medida llevado a

cabo mediante un análisis factorial exploratorio de una muestra de 350 estudiantes universitarios.

Los resultados identificaron 6 constructos a través de 24 artículos: buena disposición para el uso,

preferencia móvil, comunicación asincrónica, eficacia de la comunicación, afecto y

comunicación apropiada. Las relaciones de los constructos fueron sintetizadas teóricamente en

un modelo propuesto de competencia de la comunicación móvil. En un segundo estudio, el

modelo MCC fue sometido a un análisis factorial confirmatorio sobre una muestra de 212

estudiantes universitarios para poner a prueba la estructura teórica del modelo. Los resultados

indicaron que el modelo MCC predice un 59% de la varianza en el uso del teléfono móvil.

Palabras claves: Comunicación Móvil; Teléfono Móvil; Teléfono Celular; Competencia; CMC;

Comunicación mediada por la computadora; Competencia Interpersonal; Competencia

Comunicacional; Fluidez Tecnológica

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