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HOW INDIVIDUAL DIFFERENCES INFLUENCETECHNOLOGY USAGE BEHAVIOR? TOWARD

AN INTEGRATED FRAMEWORK

YUANDONG YI, ZHAN WU, and LAI LAI TUNGNanyang Technological University

Singapore 639798

ABSTRACT

Previous studies suggest that individual differences havemain effects on technology use and that they also interact withperceptions about technologies to influence technology use.However, few studies examine both effects simultaneously andthus prior research offers only a partial glimpse of the wholepicture. The purpose of this study is to incorporate individualdifferences into TAM and examine the two effectssimultaneously. Online survey method was used to collect data.Results from quantitative analyses indicate that individualdifferences may influence technology use directly or indirectlyvia perceptions and that they may also moderate therelationships between perceptions and technology use. Based onthe fmdings, we proposed an integrated framework, whichsuggests that individual differences influence technology use inmultiple ways. Such a framework offers a comprehensiveunderstanding of how individual differences influencetechnology use and thus provides a foundation on whichresearch models can be theorized and empirically validated.

Keywords: individual differences, perceptions, technologyuse. integrated framework,

INTRODUCTION

As documented in literature (27). information technologiesare often not fully utilized by mainstream customers as much asthey should. Consequently, examining what factors influence an

individual's use of information technologies and how thesefactors influence technology usage behavior remains critical.Lakhanpal (21) reviewed the literature on innovation inorganizations and developed a framework which indicates thatthe use of IT is influenced by individual factors, organizationalfactors, characteristics of IT. and environment factors. In thisstudy, we focus on the first category, i.e. individual factors,following the recent interest in the effect of individualdifferences on the diffusion of IT (4. 33). Actually, researchershave shown that individual differences are important totechnology use. For example. Gefen and Straub found that malesand females have different perceptions about ease of use andusellilness toward email systems and thus have different emailsystem usage behavior (13). Dabholkar and Bagozzi found thatpersonal traits such as inherent novelty seeking influence thesalient of attitude toward using a technology in determining theintention to use that technology (10).

Although individual differences are important tounderstand technology usage behavior, IT adoption models suchas technology acceptance model (11) have not paid sufficientattention to individual difference variables (4). Since technologyacceptance model (TAM) appears to be the most widelyaccepted models that explain the relationship betweenperceptions and technology use (4), in the study we try toincorporate individual differences into TAM and explore howindividual differences in combination with perceptions aboutnew technologies influence technology usage behavior.

FIGURE IFirst Research Stream and Conceptual Model I

Individualci^'ereoces

Tochrology

FIGURE 2Second Research Stream and Conceptual Model I I

Pt»rrftptiors

ncivicuald fferences

r Tcchr i lopv

Winter 2005-2006 Journal of Computer Information Systems 52

As to how individual differences combine with perceptionsto influence technology use, at least two research streams can beidentified. The first research stream (Figure 1) posits thatindividual differences influence technology use indirectlythrough perceptions (4.12). This research stream is based on thetheory of reasoned action (5), which posits that externalvariables such as individual differences will have an effect onbehavior only to the extent that they influence the determinantsof that behavior. The second research stream (Figure 2) arguesthat individual difference variables may moderate therelationships between perceptions and technology use (36). It is

interesting to note that for the same individual differencevariables researchers have argued for different mechanismsthrough which these variables influence technology use. Forinstance, while Venkatesh et al. (38) argued that gendermoderates the relationship between perceptions (such asperformance expectancy and effort expectancy) and behaviorintention. Gefen and Straub (13) argued that gender indirectlyinfluences technology use through perceived usefulness andperceived ease of use. Table 1 listed selected studies thattheorized and/or tested how individual differences combine withperceptions about technologies to influence technology use.

TABLE ISelected Research on the Impacts of Individual Differences in TAM

Studies2

4

13

20

2834353

10

3738

Individual Difference Variables in Research ModelSelf-efficacy, cognitive ahsorptionRole with regard to technology, tenure in workforce, level of education,prior similar experiences, and participation in trainingGenderPersonal innovativeness, written communication apprehension, oralcommunication apprehension, and computer anxietyAgeComputer self-efficacy, computer anxietyComputer self-etTicacyPersonal innovativenessSelf-efTicacy. inherent novelty seeking, need for interaction, self-consciousnessGenderGender, age, experience, voluntaries

1'* Research Stream

V

V>/

2"^ Research Stream

Although studies suggest that individual differencevariables may influence technology use through perceptions(main effect) and that such variables may also moderate therelationships between perceptions and technology use(interaction effect), most studies have focused on one of the twotypes of effect and neglected the other. For example, Venkateshet al proposed a unified view of user acceptance of IT. But theytheorized only the moderating effects of gender, age. andexperience (38) and omitted the main effects. However, studies(13, 28) have already showed that these individual differencevariables also have direct impacts on technology use. Hence, toaddress the need for a more comprehensive approach to this areaof inquiry, this study examines the above-mentioned two typesof effect in one study and explores a range of individualdifference variables in line with prior research. Furthermore, asfar as we can discern, there is no comprehensive generalframework in the literature to serve as a guide for researchersinterested in exploring the effects of individual differencestogether with perceptions on technology use. Thus, in the paper,we explore the main effect and interaction effect of a series ofindividual difference variables and propose an integratedframework to explain possible ways in which individualdifference variables combine with perceptions to influencetechnology usage behavior.

Accordingly, we explore the following research question inthis study:• Do individual differences influence technology usage

behavior through both mechanisms represented inconceptual model I (Figure 1) and model II (Figure 2)?Although Agarwal and Prasad (4) tried to clarify the

process through which individual difference factors influenceinformation technology use, their theorization is based only on

the first research stream discussed early on. Since previousstudies do suggest another process through which individualdifferences influence technology usage behavior (see Figure 2),Agarwal and Prasad's study (4) is incomplete yet useful. Theobjective of this paper is to tie the various strands of researchtogether in a single framework of individual difference andtechnology use and thus offer a view of the whole rather than aview of the part. We believe our study is a natural extension toAgarwal and Prasad's work.

Consistent with Agarwal and Prasad (4), individualdifference variables include personal traits, demographicvariables, and situational variables that account for differencesattributable to circumstances such as experience and training.The following individual variables were identified as variablesof interest, i.e. gender, age, computer experience, and personalinnovativeness. Gender and age were selected as demographicvariables, computer experience was selected as a situationalvariable, and personal innovativeness was selected as a variableof personal trait.

In this study, we chose perceived ease of use (PEOU) andperceived usefulness (PU) as our variables of interest. We chosethe two perception variables because they had been widelyexamined to explain technology usage behavior (USE) in ISliterature. Furthermore, the two variables are major variablesused by TAM and its extension (11, 36) to explain technologyacceptance.

The remainder of the paper is organized as follows. First,two research models were presented, with one based on the firstreseiu-ch stream and the other based on the second researchstream. This is followed by hypotheses, which basically statethat the four individual difference variables influencetechnology use through perceptions about new technologies and


that they also moderate the relationships of perceptions withtechnology use. Whether these hypotheses are empiricallysupported or not informs us of the answer to our researchquestion. Next, we describe the methodology used in the studyfollowed by data analysis. SEM and subgroup analysis wereused to test the two research models respectively. Finally,discussions, conclusions, implications, and limitations arediscussed.

RESEARCH FRAMEWORK

Technology Acceptance Model (TAM) (1,11) is one of themost widely examined models establishing causal relationshipsbetween perceptions and new technology usage. However,researchers (4) have pointed out that TAM failed to explicitlyconsider a set of important constructs, namely individualdifferences. In this study, we aimed at exploring the effects ofindividual differences on technology use.

TAM states that people's use of a new technology (USE) isfundamentally determined by two specific perceptions, namelyperceived usefulness (PU) and perceived ease of use (PEOU). Inaddition. PU is posited to be affected by PEOU. PU is defined asthe degree to which a person believes that using a particularsystem would enhance his or her job performance (11).Perceived ease of use refers to the degree to which a personbelieves that using a particular system would be free of effort

(II). To maintain model brevity, the current study ignoredattitude and behavior intention and instead studied the directeffects of perceptions on usage, which is consistent withprevious studies (I.I 1,13.25). In fact, TAM as presented in (11)by Davis included neither attitude nor bebavior intention in theresearch model. Among other studies, some omitted attitude(18.37) and some omitted both attitude and behavior intention(1,13.25).

In terms of the actual mechanisms through whichindividual differences influence technology use, Davies et al.(12) suggested that perceptions fully mediate the effects ofexternal variables such as individual differences on technologyusage behavior. On the other hand, as previously mentioned,other studies (36) have shown that individual differences maymoderate the relationships between perceptions and technologyuse. Stemming from the two research streams discussed earlier,we proposed the following two research models which weempirically tested in this study. Research model I shown inFigure 3. which is based on research stream 1. posits that PU andPEOU fully mediate the impacts of gender, age. computerexperience, and personal innovativeness on technology usagebehavior. Research model II shown in Figure 4, which is basedon research stream II, posits that gender, age, computerexperience, and personal innovativeness moderate relationshipsbetween perceptions and technology usage behavior.Hypotheses are presented as follows.

Gender

Age

PersonalInnovativeness

ComputerExperience

FIGURE 3Research Model I

TechnologyUsage Behavior

Hypotheses in Research Model I

For this model, we focused on hypotheses that have beenexamined or proposed in the literature as we would like tonarrow our focus on theoretically meiuiingful and significantrelationships.

As Gefen and Straub (13) noted, "gender has beengenerally missing from IT behavior research" (p. 390).Examination of gender difference in technology use hasappeared only recently (13.37). Since it has been found thatwomen typically experience high levels of anxiety in usingcomputers compared with men (29). and that computer anxietyand computer self-efTicacy negatively correlate (19). "higherlevels of computer anxiety' among women can be expected tolead to lowering of self-efficacy, which in turn could lead tolower of ease of use perceptions" (37. p.119). Furthermore,men's relative tendency to feel more at ease with computers hasbeen demonstrated in IS literature by Gefen and Straub (13),

who found that males perceived more ease of use of e-mail thanfemales. Following their argument, we propose that males willperceive a new technology to be more useful than females, atleast in terms of saving the effort needed to use the newtechnology.HI:a) Women's perception of ease of use of a new technology

will be lower than men's;b) Women's perception of usefulness of a new technology

will be lower than men's.Previous studies have shown that age is associated with

difficuhy in processing complex stimuli and allocating attentionto information on the job (31). Morris and Venkatesh (28) notedthat older individuals appear to have problems with bothaccessing and retrieving information fi-om memory. Lowerperceived ease of use. a known antecedent of perceivedusefulness, by older people may result in lower perceivedusefulness.


FIGURE 4Research Model II

PerceivedUsefulness

Gender AgePersonal

InnovativenessComputer

Experience

PerceivedEase of Use

TechnologyUsage Behavior

H2:a) a newOld individuals' perception of ease of use of

technology will be lower than young individuals',b) Old individuals' perception of usefiilness of a new

technology will be lower than young individuals'.Hackbarth et al. (14) found that users perceive a system

easier to use as they gain more knowledge and confidencethrough direct experience in using the system. In the same vein,we expect that people with more computer experience will alsohave higher perceived ease of use of a new technology in thatmore experience with computers was found to be associatedwith higher computer skills (15). Hartzel (16) found thatpeople's self-efflcacy concerning their ability to use a specificcomputer program improves after a simple tutorial, whichsuggests that people with much computer experience will havehigher levels of self-efficacy and ultimately perceive a newtechnology to be easier to use tban those with little computerexperience. We also expect that general computer experiencewill have a positive influence on perceived usefulness. This ishecause I) more computer experience will decrease the effortsneeded to use the new technology and 2) individuals with muchcomputer experience are more likely to explore the usefulfunctions offered by the new technology.H3:a) Computer experience has a positive association with

PEOU:b) Computer experience has a positive association with PU.

A few studies suggest that innovativeness may influenceperceptions regarding a new technology. For example, Agarwaland Karahanna (2) found that personal innovativeness has apositive influence on cognitive absorption, which is anantecedent of perceived ease of use and perceived usefulness. Inexamining the effects of individual differences on the perceivedrelative advantage, a concept akin to perceived usefulness, ofGSS, Karahanna et al. (20) found that innovativeness ispositively related to perceptions of relative advantage.Accordingly, we expect that innovativeness positively relates toboth perceived ease of use and perceived usefulness.H4a) Innovativeness has a positive association with PEOU;

b) (nnovativeness has a positive association with PU.

Hypotheses in Research model II

Based on the notion that women typically display lowercomputer aptitude and higher levels of computer anxiety andthat computer self-efficacy, which is a known determinant ofperceived ease of use, is inversely correlated with anxiety,Venkatesh and Morris (37) concluded that women tend to havelower levels of perceived ease of use than men. Moreover,considering low ease of use is typically a hurdle to useracceptance, perceived ease of use was hypothesized to be moresalient for women than for men (35).M5a) The influence of PEOU on technology use will he

moderated by gender, such that PEOU will influencetechnology use more strongly for women than for men;

Based on Minton and Schneider's (26) argument that menmay he more task oriented than women, factors that are relatedto productivity enhancement are expected to be more salient formen than for women (37). Consequently, perceived usefulnesswill influence technology use more strongly for men than forwomen.H5b) The influence of PU on technologv' use will be

moderated by gender, such that PU will influencetechnology use more strongly for men than for women.

Morris and Venkatesh (28) found that age moderates therelationship between perceived behavior control and usage suchthat older workers place greater emphasis on perceived behaviorcontrol than younger workers. Here, perceived behavior controlwas defined as "people's perception of the ease or difficult ofperforming tbe behavior of interest" (p.377). The underlyinglogic is that older workers place greater importance on receivinghelp and assistance on the job than younger workers. Based onthe same logic, we hypothesized thatH6a) The influence of PEOU on technology use will be

moderated by age, such that PEOU will influencetechnology use more strongly for old individuals than foryoung individuals;

Venkatesh et al. (38) argued that since younger people mayplace more importance on extrinsic rewards, the influence of


performance expectancy on behavior will be stronger foryounger people than for older people. They also noted thatconstructs related to effort expectancy will be strongerdeterminants of individuals' behavior for older individuals thanfor younger ones since older individuals may have difficulty inprocessing complex stimuli and allocating attention toinformation on a task. Hence, we had the following hypothesis:H6b) The influence of PU on technology use will be

moderated by age. sucb tbat PU will influencetechnology use more strongly for young individuals thanfor old individuals.

Venkatesh et al. (38) found that the effect of effortexpectancy on behavior intention to use a new technology wasstronger for those who have little experience with thetechnology than for those who have much experience. Thereasoning is as follows. Individuals without experience mayfocus first on ease of use, and with experience, they haveovercome concerns about ease of use and may focus theirattention on other factors (32). Based on the same logic, wehypothesized thatH7a) The influence of PEOU on technology use will be

moderated by computer experience, such that PEOU willinfluence technology use more strongly for those withlittle computer experience than for those with muchcomputer experience.

The moderating role of innovativeness on the relationshipsbetween perceptions and use of technologies has been theorizedin a few studies. Individuals with high levels of innovativenesswith respect to technologies have stronger intrinsic motivation touse new technologies and enjoy the stimulation of trying newtechnologies. Compared with less innovative individuals.innovative individuals would not be greatly concerned aboutwhether the new technologies are easy to use and may still trythem despite the possible difficulties in using them (10).Therefore, PEOU would not be quite so important to them, as itwould be to individuals with tow levels of innovativeness.H8a) Innovativeness will moderate the relationship between

PEOU and technology use. such that PEOU willinfluence technology use more strongly for people withlow levels of innovativeness than for those with highlevels of innovativeness;

Trying new technologies is arguably associated with greatrisks and uncertainties. Innovators, however, are able to copewith and prone to higher level of risks and uncertainties (3).Thus, for the same level of new technology usage behaviors,individuals with higher levels of innovativeness would requirelower levels of positive perceptions such as PU. than lessinnovative individuals.H8b) Innovativeness will moderate the relationship hetween

PU and technology use, such that PU will influencetechnology use more strongly for people with low levelsof innovativeness than for those with high levels ofinnovativeness.

RESEARCH METHODOLOGY

A field study was conducted to test the research models.Undergraduates of the business school in a local university werechosen as our research subjects. We chose them because theyfitted our requirements for subjects. First of all, they were beingintroduced to a new technology (in this case, a statisticalprogram). Training was a part of the course, and students wereprovided with data to practice either in class or after class.

Secondly, before the introduction of the new technology,subjects bad no prior knowledge of the program. Thirdly, use ofthe statistical program was completely voluntary for subjectsduring the period we conducted the study. Lastly, individualswith college education are major users of the statistical programand undergraduates represent a significant body of the end usersof the program concerned. Furthermore, researchers haveshowed that introducing new technologies is a challenge toorganizations as well as in the classrooms (24) and students areconsidered appropriate when they fit research's objective (17,22).

The lecturer of this course and lecturers of other coursesdid not assign those students any homework that might involvethe use of the statistical program at the time when we collecteddata. ITie students were trained in the very basics of the programsuch as correlation, mean, standard deviation, etc. Accordingly,if they want to conduct such kind of analyses, they can use thestatistical package taught and they also had other alternatives.For example, they can use Microsoft Excel to calculate means.

Online survey was used to collect data (part of the onlinesurvey is shown in appendix). The web pages were designed sothat subjects were comfortable about the online survey. Wechose online survey method because it has lower costs and fasterresponses over traditional paper-based mail surveys and it isgaining acceptance in IS research (6). Tbree rounds of pilot testswere used to improve the online survey. In each round, twohusiness graduate students were asked to complete the onlinesurvey and comment on the design. They were also encouragedto suggest ways in which subjects would fee! more comfortableabout the survey. Several rounds of revisions were made basedon their feedbacks.

Measurement

PU and PEOU were measured using items adapted from(36). Items of personal innovativeness were from Parasuraman(30). We measured all these constructs using 5-point Likert scale(strongly disagree=l, strongly agree=5). To measure subjects'computer experience, we asked them to indicate how long (inyears) they had used computers. We operationalized technologyuse as frequency of technology use, which is consistent withliterature (11,12,28,37). We asked subjects how many times theyused the statistical program every week, on average. The waywe measured self-reported system usage was consistent withprevious studies (1).

Procedure

Subjects in this study were 89 second-year undergraduates.At first, the subjects received three sessions of training with twohours each session and one session each week. Training contentincluded data entry, data cleaning, correlation calculation, aswell as descriptive analyses such as mean, standard deviation,distribution, etc. Three weeks after the students were firstintroduced to the program, we administered the online survey.Just before the class, we briefly introduced the research projectto the students, informed them the address of our online survey,and asked them to complete the survey on the spot. Eighty ninesubjects submitted the online survey. One subject did notcomplete the survey. Thus we received 88 usable cases. Samplecharacteristics were shown in table 2. About 60% of the sampleis female, which is typical of the business student body at thisuniversity.

Winter 200S-2006 Journal of Computer Information Systems 56

Note1)2)

TABLE 2Sample Characteristics

AgeEXPGender

Mean20.847.50FemaleMale

S.D.1.0822.39358%42%

Median207.50

Maximum2315

Minimum192

Missine0000

EXP=Years of computer experienceSample Size N=88

DATA ANALYSIS

Measurement Model

Psychometric properties of the perceived usefiilness,perceived ease of use, and innovativeness were assessed in termsof discriminant validity and internal consistency.

Factor analysis was performed to ascertain that perceivedusefulness, perceived ease of use. and innovativeness are distinctconstructs. One item (INN5) was deleted from theinnovativeness scale because of its low loading. Results fromPLS are reported in Table 3. The PLS results confirm that each

of these constructs is unidimensional and distinct and that allitems used to operational ize a particular construct load onto asingle factor. As a measure of internal consistency, compositereliability was calculated for all the constructs. Results show thatthe composite reliability of all constructs is higher than 0.85.The facts that all average variance extracted (AVE) is greaterthan 0.50 and that the square root of AVE is greater than thecorrelations among the latent variables (Table 3) suggestdiscriminant validity was established. Descriptive statistics forthe research constructs and inter-construct correlations are alsoshown in Table 4.

TABLE 3Loadings and Cross-Load ings for the Measurement Model, Results from PLS

INNlINN2INN3rNN4INN6INN7PEOUIPE0U2PEOU3PEOU4PUlPU2PU3PU4Composite Reliability

Loadines and Cross-loadingsINN

0.760.590.780.760.810.700.220.3!0.290.170.220.100.170.150.88

PEOU0.150.210.200.220.290.220.810.730.830.780.190.170.170.270.87

PU0.130.120.10

-0.060.260.080.150.060.170.300.870.920.910.890.94

Note:!) INN-innovativeness, PEOU=Perceived Ease of Use. PU=Perceived Usefiilness

STRUCTURAL MODEL

Research Model I

The data were analyzed using PLS Graph Version2.91.03.04 to test research model 1. After we considered thetrade-off between computational time and efficiency (8),bootstrap resampling (500 resamples) was chosen since

computational time is not a problem for us and we pursuedhigher efilciency. Bootstrapping method was also used by otherIS researchers such as Venkatesh et al. (38). The pathcoefficients and explained variances for research model I wereshown in Figure 5. Results from Figure 5 show that the onlysignificant links between individual differences and perceptionsare AGE-PU and innovativeness-PEOU relationships. Genderand computer experience do not affect either PU or PU.


TABLE 4Descriptive Statistics and Inter-Construct Correlations

AgeGenderEXPINNPEOUPUTechnology UseMeanStandard Deviation

Age

-0.770.100.280.200.180.12

20.841.08

Gender

-0.08-0.30-0.24-0.070.02

EXP

0.260.160.00

-0.217.502.39

INN

0.740.310.180.062.620.74

PEOU

0.790.230.252.870.75

PU

0.90.353.410.78

Technologyof Use

0.770.58

Note:1) EXP=Computer Experience, lNN=Innovativeness, PEOU=Perceived Ease of Use. PU=Perceived Usefulness2) The shaded numbers are square roots of the Average Variance Extracted (AVE)

FIGURE 5PLS Results for Fully Mediated Model

Gender

PersonalInnovatjveness

PerceivedUsefulness

PerceivedEase of Use

0.32"

TechnologyUse

ComputerExperience

Note:1) •means relation is significant at the 0.05 level, ** means significant at the 0.01 level, *** means significant at the 0.001 level, and

dashed line means non-signitlcant relation.

Since previous studies have suggested that gender andcomputer experience do have influences on technology use, weempirically tested the partially mediated model to clarifywhether perceptions really fully mediate the influence ofindividual differences on technology use. Figure 6 shows thePLS results.

Figure 6 indicates that gender and computer experienceinfluence technology use directly. PU was found to partiallymediate the influence of age on technology use. PEOU wasfound to fully mediate the influence of personal innovativenesson technology use. Since PLS graph does not report model fitmeasures such as CFL RMS, R square was used to compare thefully and partially mediated models. As a result, the partiallymediated model (R'=0.25) explains more variance of technologyuse than does the f\illy mediated model (R^=0.15).

Research Model II

As reflected in our hypotheses, we defined moderation asdifferential prediction in the study, which is consistent withprevious studies (7). Chin et al. (9) suggested that with 4

indicators per construct a minimum sample size of 150 would heneeded to balance the trade-ofts for detection and accurateestimate of interaction using PLS. Considering most moderatorsin our study are single-item measures and our sample size issmall, we opted not to use PLS to empirically detect interaction.Rather, a series of subgroup analyses and iterations of multiplelinear regressions were performed. A suhgroup analysis toexplore moderating effect has been used in previous research(37). To reduce the threat of multicollinearity in regressionmodels, all variables were standardized. Sample was dividedinto subgroups hased on the median of the moderating variable.

Table 5 shows that gender, age, and innovativenessmoderate the effects of PU on technology use hut not the effectsof PEOU on technology use. However, computer experiencewas found to moderate the PEOU-USE relationship. Table 6summarizes our findings.

Information provided by Figures 5 and 6 suggests thatgender does have an influence on technology use and thatgender infiuences technology use directly. Specifically, femalesuse the statistical program more often than males. Table 5(a)shows that perceived usefulness is more salient for females than


for males in predicting technology use. !n sum. genderinfluences technology use via two mechanisms, i.e. it directly

influences technology use and also moderates the relationshipbetween PU and technology use.

FIGURE 6PLS Results for Partially Mediated Model

Note:1) *means relation is significant at the 0.05 level. **means significant at the O.OI level, •••means significant at the 0.001 level,

and dashed line means non-signitlcant relation.

TABLE 5Multiple Regression to Detect Moderating Effects

a) Subgroups by gender

PEOUPUDependent variable

Male0.140.28

Female0.240.35^^

Frequency of Use

b) Subgroups by age


Young0.210.33*

Old0.140.29

Frequency of Use

c) Subgrou

PEOU

Dependent variable

)s by computer expenenceLittle0.33*

Much0.16

Frequency of Use

d) Subgroups by innovativeness


Low0.170.36*

High0.160.24

Frequency oFUseNote:1) Coefficients are standardized coefficients.2) *means correlation is significant at the 0.05 level, ••means significant at the 0.01 level, and • • • means significant at the 0.001 level

Perceived usefulness partially mediates the influence of ageon technology use. Contrary to our hypotheses, our results showthat the older an individual is., the more useful he or sheperceives a new technology. Such a finding is just the oppositeof Morris and Venkatesh's (28) findings. Nevertheless, ourfinding is not unexpected considering that for college studentsage may be a driver of technology use rather than a hurdle. Ageis also found to moderate the influence of perceived usefulnessand teehnology use in the direction as we hypothesize. However,we failed to detect a moderation effect of age on the PEOU-USElink. In sum, age influences technology use also via two

mechanisms, namely 1} PU partially mediates the effect of ageon technology use. and 2) age moderates the PU-USE relation.

Our results suggest that general computer experience doesnot influence either perceived ease of use or perceivedusefulness. However., we find that experience negatively anddirectly influences technology use. This suggests that the morecomputer experience an individual has. the less he or she uses anew technology. This may be because individuals with morecomputer experience can master the new technology quickly andthus uses the new technology less. Furthermore, we find thatcomputer experience moderates the relation between PEOU and


technology use in the direction we expect. In sum. computerexperience also has two types of effects on technology use.

Specifically, it directly and negatively influences technology useand also moderates the relation of PEOU with technology use.

TABLE 6Summary of Findings

HvDothesis

HI

H2

H3

H4

H5

H6

H7

H8

HI a) not supported;HI b)not supported.H2 a) not supported;H2 b) supported (butdirection is contrary lo ourexpectation).H3 a) not supported;H3 b) not supported.

H4 a) supported;H4 b) not supported.

H5 a) not supported;H5 b) supported (butdirection is contrary to ourexpectation).

H6 a) not supported;H6 b) supported.

H7 supported.

H8 a) not supported;H8 b) supported.

Pindinfis• Gender does not influence PF.OU and PU;• Gender influences technology use directly.

• Age does not influence PEOU, However, age positively affects PU;• PU partially mediates the influence of age on technology use.

• Computer experienee does not influence PEOU and PU;• Computer experience negatively influences technology use directly.• Innovativeness positively affects PEOU. However, innovativeness does not

influence PU;• PEOU fully mediates the influence of innovativeness on technology use.

• Gender does not moderate the PEOU-USE link;• PU influences technology use more strongly for women than for men.

• Age does not moderate the PEOU-USE link;• PU influences technology use more strongly for young individuals than for

old individuals.• PEOU influences technology use more strongly for those with little

computer experience than for those with much computer experience.• Innovativeness does not moderate the PEOU-USE link;• PU influences technology use more strongly for people with low levels of

innovativeness than for those with high levels of innovativeness.

Based on information reflected in Figure 5, we concludethat perceived ease of use fully mediates the impact ofinnovativeness on technology use. Table 5(d) suggests thatinnovativeness moderates the relationship between perceivedusefulness and technology use such that the effect is stronger forless innovative persons. However, we failed to detect anymoderating role of innovativeness on PEOU-USE relationship.In sum, innovativeness also has both a main effect and aninteraction effect on technology use.

DISCUSSION AND CONCLUSION

In the study, we explored whether individual differenceshave both main effect and interaction effect on technology use.Based on our findings, we conclude that individual differencemay influence technology use directly or indirectly while theymay also moderate the relationship between perceptions andtechnology use. For example, gender and computer experienceinfluence technology use through two ways, that is. 1) theyaffect technology use directly and 2) they also moderate theperception-PU relationships. PU-USE link and PEOU-USE linkrespectively. For another example, age and personalinnovativeness have a direct impact on perceptions (PU andPEOU respectively) and they also moderate the relationshipbetween PU and USE.

Based on our findings, we propose an integrated frameworkregarding the influence of individual differences on technologyuse (Figure 7), This framework suggests that individualdifferences may influence technology use in multiple ways.Firstly, individual differences directly affect technology use(PI). Secondly, individual differences indirectly influence

technology use through perceptions (P3 and P4). Lastly,individual differences moderate the relationships betweenperceptions and technology use (P2). In other words, on onehand, perceptions may fully or partially mediate the influencesof individual differences on technology use; on the other hand.individual difTerences may moderate the relationship betweenperceptions and technology use. The challenge for researchers isto identify which individual difference variable affectstechnology use in which ways.

Implications

The study contributes to theory in the following ways. Firstof all. the study extended previous research by identifying tworesearch streams and proposing an integrated framework toguide future research regarding the impacts of individualdifferences on technology use. Our integrated frameworksuggests that individual differences may influence technologyuse in multiple ways. The research has extended previousstudies, most of which focused on one way or other. Forexample. UTAUT (38) posits that gender, age. and experiencemoderate the relations between perceptions and behaviorintentions or user behavior. Our study extended the model byshowing that these individual difference variables also havemain effects on technology usage behavior. Thus, studiesimplicating that individual differences influence technology useonly in a single way may be incomplete and inaccurate.Secondly, this research furthers our understanding of technologyuse by mapping out major contingency factors. Venkatesh andDavis (36) suggested that, "further research on TAM...should.. .continue to map out the major contingency factors moderating


the effects of perceived usefulness, perceived ease of use...onintention." We believe that our research attempt is a timelyresponse to Venkatesh and Davis's appeal. Gender, age, andinnovativeness were suggested by previous studies to be

possible moderators. We have verified the moderating effects ofthese variables in this study. We also identified that generalcomputer experience also has a moderating effect on the relationof PEOU with technology use.

FIGURE 7Mechanisms of How Individual Differences Influence Technology Use

Dfferencos

P3 /

Porsoral tratsDen'cg'ap^lc vanaa osSiMa:ior^T variables

P2

Perceived ease of jsePorceivod j&ef J ress

\

V P1\

P4

Technology Usage Bahabio'

'Bi;Ler;;y a* us

This study also contributes to practice in multipleways. Firstly, an understanding of the effects of individualdifferences on technology use is important in overcomingbarriers to the diffusion of technology across an organization.An understanding of the mechanisms through which individualdifferences influence technology usage behavior is important forreducing resistance to technology use. Secondly, because thisstudy focuses on technology use and unlike studies looking atbehavioral intention, any improvement in terms of a betterunderstanding of phenomena can translate into higheracceptance and usage of the technology afler implementation.Finally, the results of this study may contribute to ourknowledge on whether perception management will ultimatelylead to better technology use. This knowledge may have animpact on the budget and spending for interface design andtraining purposes for organizations. For example, we found thatPEOU influences technology use more strongly for those withlittle computer experience than for those with much computerexperience. Based on such a finding, if the target users of a newtechnology are predominantly individuals with little computerexperience, the technology provider should invest more inmaking the technology easier to use to facilitate user acceptanceof the new technology.

Limitations

This study also suffers from several limitations. Onelimitation is that we used student sample to test our researchmodels. Especially, the students were young (between 19 and23). lTius, researchers should be cautious when generalizing ourfindings to non-student populations. Future studies may validatethe findings of this study in other populations. Seeondly. weused subjects' self reported actual technology use. Ideally, wewould use objective data from other sources. Unfortunately,such data were unavailable fo us. Thirdly, the R of the researchmodels in this study is a little bit low, which implies that the

students' actual use of the statistical program was alsoinfluenced by variables other than those examined in this study.Future studies may include perception variables such as thoseexamined in Venkatesh et al.'s paper (38) (we did not becauseour data collection was conducted prior to the UTAUTpublication) and individual difference variables such as self-efficacy. Lastly, we proposed the integrated framework based onour exploratory study of the effects of individual differences ontechnology use. The framework should be consideredexploratory rather than confirmatory. More studies have to beconducted to validate the relevant propositions.

In conclusion, since "research indicates that the success ofan IT/IS innovation implementation depends as much onindividual diflerences as on the technology itself (23), we arguethat it is time for researchers to divert the research question from"are individual differences germane to the acceptance of newinformation technologies" (4) to that of how individualdifferences affect the acceptance and usage of new technologies.We believe our study has taken the first steps towardunderstanding how individual differences influence technologyuse.

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APPENDIXPart of the Online Survey

The following are some statements regarding the SPSS, for each one, please indicatewhether you "strongly agree." "somewhat agree." are "neutral." "somewhat disagree." or"strongly disagree'.

Using SPSS Improves my performdnce in my studiesS t r o i ^ DKagree Someii^nt Disagree Neiitnl Sonew^Mt agne S t r o n g agree

I find SPSS to be usefui in my studiesS h o i ^ Disagree Somewhat Disagree Neutral Somewhat agree Strongly agree

My interaction with SPSS is clear and understandabieS t i D i ^ Dis^ree Somewhat Disagree Neulral Somewhat agree S t r o n g agree

Interacting with SPSS does not require a iot of my mental effortS t r o n g Disagree Soiaevriiat Disagree Neutral Somewhat agree S t r o n g agree

I find SPSS to be easy to useS t i D i ^ Dis^iee Somewhat Disagree Neatial Somewhat agree Strongly agree


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