Computer Self Efficacy: Development of a Measure and ... · Computer Self- Efficacy’. Development...

Computer Self-Efficacy--Measurement

ComputerSelf- Efficacy’.Development of aMeasure and InitialTest

organizations. The existence of a reliableand valid measure of self-efficacy makesassessment possible and should have impli-cations for organizational support, training, andimplementation.

Keywords: User behavior, psychology, measure-ment, causal models, partial least squares

ISRL Categories: GB03,GB02

By: Deborah R. CompeauSchool of Business AdministrationCarleton UniversityOttawa, ON KlS [email protected]

Christopher A. HigginsSchool of Business AdministrationThe University of Western OntarioLondon, ON N6A [email protected]

AbstractThis paper discusses the role of individuals’beliefs about their abilities to competently usecomputers (computer self-efficacy) in thedetermination of computer use. A survey ofCanadian managers and professionals wasconducted to develop and validate a measure ofcomputer self-efficacy and to assess both itsimpacts and antecedents. Computer self-efficacy was found to exert a significantinfluence on individuals’ expectations of theoutcomes of using computers, their emotionalreactions to computers (affect and anxiety), well as their actual computer use. An in-dividual’s self-efficacy and outcome expect-ations were found to be positively influenced bythe encouragement of others in their workgroup, as well as others’ use of computers.Thus, self-efficacy represents an importantindividual trait, which moderates organizationalinfluences (such as encouragement and support)on an individual’s decision to use computers.Understanding self-efficacy, then, is important tothe successful implementation of systems in

IntroductionUnderstanding the factors that influence an indi-vidual’s use of information technology has beena goal of MIS research since the mid-1970s,when organizations and researchers began tofind that adoption of new technology was not liv-ing up to expectations. Lucas (1975, 1978) pro-vides some of the earliest evidence of theindividual or behavioral factors that influenced ITadoption. The first theoretical perspective togain widespread acceptance in this researchwas the Theory of Reasoned Action (Fishbeinand Ajzen, 1975). This theory maintains that in-dividuals would use computers if they could seethat there would be positive benefits (outcomes)associated with using them.

This theory is still widely used today in the IS lit-erature and has demonstrated validity. How-ever, there is also a growing recognition thatadditional explanatory variables are needed(e.g., Thompson, et al., 1991; Webster and Mar-tocchio, 1992). One such variable, examined inthis research, comes from the writings of AlbertBandura and his work on Social Cognitive The-ory (Bandura, 1986).

Self-efficacy, the belief that one has the capabil-ity to perform a particular behavior, is an impor-tant construct in social psychology. Self-efficacyperceptions have been found to influence deci-sions about what behaviors to undertake (e.g.,Bandura, et al., 1977; Betz and Hackett, 1981),the effort exerted and persistence in attemptingthose behaviors (e.g., Barling and Beattie, 1983;Brown and Inouye, 1978), the emotional re-sponses (including stress and anxiety) of the in-dividual performing the behaviors (e.g.,Bandura, et al., 1977; Stumpf, et a1.,1987), andthe actual performance attainments of the indi-vidual with respect to the behavior (e.g., Barling

M/S Quarterly/June 1995 189

Computer Self-Efficacy---Measurement

and Beattie, 1983; Collins, 1985; Locke, et al.,1984; Schunk, 1981; Wood and Bandura, 1989).These effects have been shown for a wide vari-ety of behaviors in both clinical and managerialsettings. Within a management context, self-effi-cacy has been found to be related to attendance(Frayne and Latham, 1987; Latham and Frayne,1989), career choice and dev,elopment (Betzand Hackett, 1981; Jones, 1986), research pro-ductivity (Taylor, et al., 1984), and sales per-formance (Barling and Beattie, 1983).

Several more recent studies (Burkhardt andBrass, 1990; Gist, et a1.,1989; Hill, et al., 1986;1987; Webster and Martocohio, 1992; 1993)have examined the relationship between self-ef-ficacy with respect to using computers and a va-riety of computer behaviors. These studiesfound evidence of a relationship between self-efficacy and registration in computer courses atuniversities (Hill, et al., 1987), adoption of hightechnology products (Hill, et al., 1986) and inno-vations (Burkhardt and Brass, 1990), as well performance in software training (Gist, et al.,1989; Webster and Martocchio, 1992; 1993). Allof the studies argue the need for further re-search to explore fully the role of self-efficacy incomputing behavior.This paper describes the first study in a programof research aimed at understanding the impactof self-efficacy on individual reactions to com-puting technology. The study involves the devel-opment of a measure for computer self-efficacyand a test of its reliability and validity. Themeasure was evaluated by examining its per-

formance in a nomological network, throughstructural equations modeling. A researchmodel for this purpose was developed with ref-erence to literature from social psychology, aswell as prior IS research.

The paper is organized as follows. The nextsection presents the theoretical foundation forthis research. The third section discusses thedevelopment of the self-efficacy measure. Theresearch model is described and the hypothe-ses are presented in the following section. Then,the research methodology is outlined, and theresults of the analyses are presented. The pa-per concludes with a discussion of the implica-tions of these findings and the strengths andlimitations of the research.

TheoreticalBackground SocialCognitive TheorySocial Cognitive Theory (Bandura, 1977; 1978;1982; 1986) is a widely accepted, empiricallyvalidated model of individual behavior. It isbased on the premise that environmental influ-ences such as social pressures or unique situ-ational characteristics, cognitive and otherpersonal factors including personality as well asdemographic characteristics, and behavior arereciprocally determined. Thus, individualschoose the environments in which they exist in

Person

Environment Behavior

Figure 1. Triadic Reciprocality or Reciprocal Determinism

190 M/S Quarter~y/June 1995


addition to being influenced by those environ-ments. Furthermore, behavior in a given situ-ation is affected by environmental or situationalcharacteristics, which are in turn affected by be-havior. Finally, behavior is influenced by cogni-tive and personal factors, and in turn, affectsthose same factors. This relationship, whichBandura refers to as "triadic reciprocality," isshown in Figure 1.

While Social Cognitive Theory has many dimen-sions, this research is particularly concernedwith the role of cognitive factors in individual be-havior. Bandura advances two sets of expecta-tions as the major cognitive forces guidingbehavior. The first set of expectations relates tooutcomes. Individuals are more likely to under-take behaviors they believe will result in valuedoutcomes than those they do not see as havingfavorable consequences. The second set of ex-pectations encompasses what Bandura callsself-efficacy, or beliefs about one’s ability to per-form a particular behavior. Self-efficacy influ-ences choices about which behaviors toundertake, the effort and persistence exerted inthe face of obstacles to the performance ofthose behaviors, and thus, ultimately, the mas-tery of the behaviors.

Outcome expectations have been considered bymany IS researchers. The usefulness constructmeasured by Davis (1989) and Davis, et al.(1989) reflects beliefs (or expectations) aboutoutcomes, as does the salient beliefs constructused by Davis, et al. (1989). Thompson, et al.(1991) tested a model of personal computer usebased on Triandis (1980), which included per-ceived consequences as a central determinantof behavior. Questions measuring attitudes,such as those used by Robey (1979), also fre-quently reflect outcome expectations.

While outcome expectations have been consid-ered by many IS researchers, the role of self-ef-ficacy has received less attention. A few studies(e.g., Cheney and Nelson, 1988) have tried develop measures of computer skill using self-reports. These measures would incorporatesome aspects of computer self-efficacy, but aredesigned to measure actual capability ratherthan self-efficacy. Only a handful of studies ex-pticitly consider the role of self-efficacy in com-puting behavior (Burkhardt and Brass, 1990;Gist, et al., 19~9; Hill, et al., 1987; Webster and

Martocchio, 1992; 1993). These studies provideinitial evidence that self-efficacy has an impor-tant influence on individual reactions to comput-ing tech.nology.

Defining self-efficacyBandura (1986) defines self-efficacy as:

People’s judgments of their capabilities toorganize and execute courses of action re-quired to attain designated types of perform-ances. It is concerned not with the skills onehas but with judgments of what one can do withwhatever skills one possesses (p. 391 ).

This definition highlights a key aspect of theself-efficacy construct. Specifically, it indicatesthe importance of distinguishing between com-ponent skills and the ability to "organize andexecute courses of action." For example, in dis-cussing driving self-efficacy, Bandura (1984)

.distinguishes between the component skills(steering, braking, signalling) and the behaviorsone can accomplish (driving in freeway traffic,navigating twisting mountain roads). Similarly,Collins (1985) distinguishes between the com-ponent skills of mathematics (choice of opera-tions and basic arithmetic skills) and mathe-matics behaviors (solving particular word prob-lems). Thus, computer self-efficacy representsan individual’s perceptions of his or her ability touse computers in the accomplishment of a task(ie., using a software package for data analysis,writing a mailmerge letter using a word proces-sor), rather than reflecting simple componentskills (ie., formatting diskettes, booting up computer, using a specific software feature suchas "bolding text" or "changing margins").

The concept of self-efficacy, while representing¯ a unique perception, is similar to a number ofother motivational constructs such as effort-per-formance expectancy (Porter and Lawler, 1968),locus of control, and self-esteem. Gist (1987)provides a detailed discussion of the similaritiesand differences between self-efficacy and theseother motivational constructs.

Dimensions of Self-Efficacy

In defining self-efficacy, it is also important toconsider the relevant dimensions of self-efficacyjudgments. Self-efficacy judgments differ on

MIS Quarterly~June 1995 19’1


three distinct, but interrelated, dimensions: mag-nitude, strength, and generalizability. The mag-nitude of self-efficacy refers to the level of taskdifficulty one believes is attainable. Individualswith a high magnitude of self-efficacy will seethemselves as able to accomplish difficult tasks,while those with a low self-efficacy magnitudewill see themselves as only able to execute sim-ple forms of the behavior.

Self-efficacy strength refers to the level of con-viction about the judgment. It also reflects theresistance of self-efficacy to apparently discon-firming information (Brief and Aldag, 1981). Indi-viduals with a weak sense of self-efficacy will befrustrated more easily by obstacles to their per-formance and will respond by lowering their per-ceptions of their capability. By contrast,individuals with a strong sense of efficacy willnot be deterred by difficult problems, will retaintheir sense of self-efficacy, and as a result oftheir continued persistence are more likely toovercome whatever obstacle was present.

Generalizability of self-efficacy indicates the ex-tent to which perceptions of self-efficacy are lim-ited to particular situations. Some individualsmay believe they are capable of performingsome behavior, but only under a particular set ofcircumstances, while others might believe theycan execute the particular behavior under anycircumstances and also perform behaviors thatare slightly different.

Computer Self-Efficacy

Computer self-efficacy, then, refers to a judg-ment of one’s capability to use a computer. It isnot concerned with what one has done in thepast, but rather with judgments of what could bedone in the future. Moreover, it.does not refer tosimple component subskills, like formatting disk-ettes or entering formulas in. a spreadsheet.Rather, it incorporates judgments of the ability toapply those skills to broader tasks (e.g., prepar-ing written reports or analyzing financial data).Below, the dimensions are defined further in thecontext of computer self-efficacy.

Magnitude. The magnitude of .computer self-ef-ficacy can be interpreted to reflect the level of

capability expected. Individuals with a high com-puter self-efficacy magnitude might be expectedto perceive themselves as able to accomplishmore difficult computing tasks than those withlower judgments of self-efficacy. Alternatively,computer self-efficacy magnitude might begauged in terms of support levels required toundertake a task. Individuals with a high magni-tude of computer self-efficacy might judge them-selves as capable of operating with less supportand assistance than those with lower judgmentsof self-efficacy magnitude.

Strength. The strength of a computer self-effi-cacy judgment refers to the level of convictionabout the judgment, or the confidence an indi-vidual has regarding his or her ability to performthe various tasks discussed above. Thus, notonly would individuals with high computer self-efficacy perceive themselves as able to accom-plish more difficult tasks (high magnitude), butthey would display greater confidence abouttheir ability to successfully perform each ofthose behaviors.

Generalizability. Self-efficacy generalizabilityreflects the degree to which the judgment is lim-ited to a particular domain of activity. Within acomputing context, these domains might beconsidered to reflect different hardware andsoftware configurations. Thus, individuals withhigh computer self-efficacy generalizabilitywould expect to be able to competently use dif-ferent software packages and different computersystems, while those with low computer self-effi-cacy generalizability would perceive their capa-bilities as limited to particular software packagesor computer systems.

Development of a ComputerSelf-Efficacy MeasureThe first step in our research program involvedthe development of a measure of computer self-efficacy. A few studies had previously measuredself-efficacy in a computing context (Burkhardtand Brass, 1990; Gist, et al., 1989; Hill, et al.,1986; 1987; Webster and Martocchio, 1992;1993), and a review of these measures contrib-uted to the development of the current measure.

192 MIS Quarterly~June 1995


Previous approaches to computerself-efficacy measurementA review of the literature concerning self-effi-cacy and computers uncovered five existingmeasures. One utilized a three-item scale tomeasure computer self-efficacy in a study of theearly adoption of computing technologies (Burk-hardt and Brass, 1990). This measure request-ed general perceptions about an individual’sability to effectively use computers in his or herjob. Another measure studied the influence ofcomputer self-efficacy on enrollment in a com-puter course (Hill, et al., 1987). The measureused a four-item scale, revised from a scaleused in an earlier study (Hill, et al., 1986). Thismeasure did not, however, appear to be meas-uring self-efficacy. Three of the items usedmeasured general perceptions about the natureof computing, such as "only a few experts reallyunderstand how computers work." Responsesto these statements may or may not reflect com-puter self-efficacy. Still another measure studiedthe influence of computer self-efficacy on train-ing performance (Webster and Martocchio,1992; 1993). A five-item scale was developed tomeasure software efficacy, based on work byHollenbeck and Brief (1987). This measure,while it does seem to capture elements of self-efficacy, also incorporated other concepts, inaddition to self-efficacy. For example, one item,used to measure self-efficacy before training,asked the respondents the extent to which theyagreed with the statement "1 expect to becomevery proficient at using WordPerfect merging."Responses to this item would also reflect ex-pectations of the quality or content of thetraining program and might reflect elements ofinterest (in becoming proficient at WordPer-fect merging).

The last two measures studied the relationshipbetween computer self-efficacy, computer train-ing methods, and training performance, andboth were developed by Gist, et al. (1989). Thefirst concerned the general construct, computerself-efficacy. The second focused on a measurespecific to using a spreadsheet package. Nei-ther of the measures could be considered taskfocused. Many of the items reflected componentskills, such as the ability to boot up a computerfrom diskette or the ability to enter formulas in a

spreadsheet cell, rather than the potential to usethe software in the accomplishment of a task.

This examination of existing measures of com-puter self-efficacy indicated the need for addi-tional development work. The Gist, et al.measure focused on component skills of behav-ior rather than assessments of one’s ability tocarry out some task and is thus an inadequatereflection of self-efficacy. The Hill, et al. meas-ure, and to some extent the Webster and Mar-tocchio measure, incorporated other constructsin addition to self-efficacy. Thus, while existingmeasures could provide useful insights into themeasurement of computer self-efficacy, we felt itwas important-to develop a measure that wouldovercome these limitations.

The current approach tomeasurementIn developing a new measure of computerself-efficacy, reference was made to the exist-ing measures, in particular the works of Gist,et al. (1989), Webster and Martocchio (1992;1993), and Burkhardt and Brass (1990). Whileeach of these measures has some limitations,they offer insights that were incorporated intoour measure.

Based on these existing scales and the defini-tion of computer self-efficacy as an individual’sperception of his or her ability to use a computerin the accomplishment of a job task, a 10-itemmeasure of computer self-efficacy was devel-oped (see Appendix). The measure is task fo-cused. That is, it does not reflect simple com-ponent skills like starting software packages andsaving files. The measure also incorporates ele-ments of task difficulty (in the different levels ofsupport presented in each item) that capture dif-ferences in self-efficacy magnitude. This ap-proach is similar to one taken by Frayne andLatham (1987) in measuring attendance self-ef-ficacy, and by Condiotte and Lichenstein (1981)and Diclemente (1981) in measuring self-effi-cacy concerning smoking cessation. Self-effi-cacy strength is captured in the response scale(which measures level of confidence in the judg-ments of ability). In our study, self-efficacy gen-eralizability is not directly studied. The focus forthis work is on self-efficacy with respect to using

M/S Quarterly/June 1995 193


Encouragementby OthersComputer 1~--~~

Affect

Self-Efficacy

~~

_

Others’ Use.

Outcome ,~~~lSupport Expectations~ ( Usage

Figure 2. Research Model

computers in general. Future research is neededto assess generalizability.

Scoring the self-efficacy measure can be ac-complished in two ways. First, the number of"YES" answers can be counted, which providesan indication of self-efficacy magnitude. Second,the responses on the confidence scale can besummarized, counting 0 for a "NO" response.This approach encompasses both self-efficacymagnitude and strength and is thus most usefulfor data analysis.

Research Model andHypothesesThe research model tested in this study (Figure2) was developed with reference to the SocialCognitive Theory literature, and the existingbase of research in the information systems lit-erature. As noted earlier, Social Cognitive The-ory posits an ongoing reciprocal interactionbetween cognitive factors, environment and be-havior. While the richness of this conceptualiza-tion cannot be completely conveyed in a linear,recursive model, important insights into the rela-tionships among such variables can beachieved nonetheless.

Elements of all three forces (cognitive, environ-mental, and behavioral) were incorporated intothe research model. The choice of which spe-cific elements to include for each factor wasbased on existing IS research. Thus, the modelhelps to integrate the findings from previous ISresearch by considering several key constructswithin the context of Social Cognitive Theory. AsFigure 2 indicates, the encouragement of use byothers in the individuals’ reference group, theactual use of computers by others in that refer-ence group, and the organizational support forcomputer use, are each held to influence self-ef-ficacy and outcome expectations. Self-efficacyinfluences usage directly, as well as indirectly,through outcome expectations, affect, 1 andanxiety. Outcome expectations influence usagedirectly, as well as indirectly, through affect.Finally, affect and anxiety are each held to in-fluence usage. Thus, the research model encom-passes 14 hypotheses regarding individualreactions to computing technologies. The fol-lowing paragraphs state the theoretical ration-ale for each of these hypotheses. Empirical¯ evidence from both the Social Cognitive Theory

The term affect is used throughout this study to refer topositive affect. Negative affect was not investigated.

t94 MIS Quarterly/June 1995


and information systems literature is used to re-inforcethesearguments.

Encouragement by others

The encouragement of others within the individ-ual’s reference group--the people to whom anindividual looks to obtain guidance on behav-ioral expectations--can be expected to influ-ence both self-efficacy and outcome expectat-ions. Encouragement of use represents "verbalpersuasion," one of the four major sources of ef-ficacy information (Bandura, 1986). Individualsrely, in part, on the opinions of others in formingjudgments about their own abilities. Thus, en-couragement from others influences self-effi-cacy, if the source of encouragement isperceived as credible (Bandura, 1986). Encour-agement of use may also exert an influence onoutcome expectations. If others in the referencegroup, particularly those in the individual’s workorganization, encourage the use of computingtechnology, the individual’s judgments about thelikely consequences of the behavior will be af-fected. At the very least, the individual will ex-pect that his or her coworkers will be pleased bythe behavior. Thus, the first two hypotheses areas follows:Ht: The higher the encouragement of use by

members of the individual’s referencegroup, the higher the individual’scomputer self-efficacy.

H2: The higher the encouragement of use bymembers of the individual’s referencegroup, the higher the individual’s out-come expectations.

Others’ useEncouragement of use is one source of influ-ence on self-efficacy and outcome expectations.The actual behavior of others with respect to thetechnology is a further source of informationused in forming self-efficacy and outcome ex-pectations. Learning by observation, or behaviormodeling, has been shown to be a powerfulmeans of behavior acquisition (Latham andSaari, 1979; Manz and Sims, 1986; Schunk,1981). Behavior modeling influences behavior inpart through its influence on self-efficacy (Ban-

dura, et al~ 1977) and also through its influenceon outcome expectations by demonstrating thelikely consequences of the behavior (Bandura,1971). Thus, Hypotheses 3 and 4 reflect the in-fluence of the modeling behavior of others in theindividual’s reference group:H3: The higher the use of the technology by

others in the individual’s referencegroup, the higher the individual’s com-puter self-efficacy.

H4: The higher the use of the technology byothers in the individual’s referencegroup, the higher the individual’s out-come expectations.

Support

The support of the organization for computer us-ers can also be expected to influence individu-als’ judgments of self-efficacy. The availability ofassistance to individuals who require it shouldincrease their ability and thus, their perceptionsof their ability. Support can also be expected toinfluence outcome expectations because thissupport reflects the formal stance of the organi-zation toward the behavior, and may thereforeprovide clues about the likely consequences ofusing the computer. Thus, Hypotheses 5 and 6are as follows:H5: The higher the support for computer

users in the organization, the higher theindividual’s computer self-efficacy.

H6: The higher the support for computerusers in the organization, the higher theindividual’s outcome expectations.

Computer self-efficacy

Social Cognitive Theory affords a prominent roleto self-efficacy perceptions. Self-efficacy judg-ments are purported to influence outcome ex-pectations since "the outcomes one expectsderive largely from judgments as to how wellone can execute the requisite behavior" (Ban-dura, 1978, p. 241). The hypothesis is:

H7: The higher the individual’s computerself-efficacy, the higher his/her out-come expectations.

MIS Quarterly/June 1995 195


Self-efficacy judgments are held to have a sub-stantial influence on the emotional responses ofthe individual. Individuals will tend to prefer andenjoy behaviors they feel they are capable ofperforming and to dislike those they do not feelthey can successfully master. Several studies inpsychology provide support for this contention.One found that self-efficacy perceptions weresignificantly related to affect (or interest) for par-ticular occupations (Betz and Hackett, 1981).Others found that individuals experience anxietyin attempting to perform behaviors they do notfeel competent to perform (Bandura, et al.,1977); Stump/, et al., 1987). These relationshipsare predicted by Hypotheses 8 and 9 as follows:

H8: The higher the individual’s computerself-efficacy, the higher his/her affect(or liking) of computer use.

H9: The higher the individual’s computerself-efficacy, the lower his/her computeranxiety.

Self-efficacy perceptions are predicted to be asignificant precursor to computer use. This hy-pothesis is supported by research regardingcomputer use (Burkhardt and Brass, 1990; Hill,et al., 1987) and research in a variety of otherdomains (Bandura, et al., 1977; Betz and Hack-ett, 1981; Frayne and Latham, 1987). While self-efficacy has not been explicitly measured inother IS research, there is some evidence tosupport the influence of self-efficacy. Maish(1979) included a variable that measured theextent to which the user felt "prepared to use"the new system. This variable is conceptuallyquite similar to self-efficacy and was found to berelated to the degree of use. Similarly, the ’~Nill-ingness to change" construct measured byBarki and Huff (1990), which in part reflects self-efficacy, was found to be related to use of a de-cision support system.

H10: The higher the individual’s computerself-efficacy, the higher his/her use ofcomputers.

Outcome expectations

Outcome expectations also exert a significantinfluence on individuals’ reactions to computingtechnology. The expected consequences of a

behavior may be construed as an influence onaffect (or liking) for the behavior, through a proc-ess of association. The satisfaction derived fromthe favorable consequences of the behaviorbecomes linked to the behavior itself, causingan increased affect for the behavior (Bandura,1986). This gives dse to the following hypothesis:

Hl1: The higher the individual’s outcomeexpectations, the higher his/her affect(or liking) for the behavior.

Outcome expectations are also an importantprecursor to usage behavior. According to So-cial Cognitive Theory, individuals are more likelyto engage in behavior they expect will be re-warded (or will result in favorable conse-quences). Support for this contention can befound in a study of aggressive behavior in chil-dren (Bandura, 1971). The hypothesis is alsosupported by IS research (Davis, et al., 1989;Hill, et al., 1987; Pavri, 1988; Thompson, et al.,1991). Thus, the hypothesis is:

Ht2: The higher the individual’s outcomeexpectations, the higher his/her use ofcomputers.

AffectIndividuals’ affect (or liking) for particular behav-iors can, under some circumstances, exert astrong influence on their actions. Televisionpreferences, for example, are almost solelybased on affect (Bandura, 1986). Consumerchoices are also often made on the basis of af-fective reactions (Engle, et al., 1986). With re-spect to computersl the evidence is not clear.Thompson, et al. (1991), for example, found relationship between affect for PC use and theuse of personal computers among managers.However, as the authors acknowledge, themeasure of affect was somewhat weak, andthus, the finding may simply be a reflection ofthe measurement.

Given the theoretical support for such a link,within Social Cognitive Theory and other behav-ior theories (ie., Fishbein and Ajzen, 1975; Tri-andis, 1980), the relationship between anindividual’s liking for computer use and his orher actual behavior is worthy of further study.Thus, the next hypothesis is:

’196 MIS Quarterly/June 1995


H13: The higher the individual’s affect forcomputer use, the higher h’islher use ofcomputers.

Anxiety

Feelings of anxiety surrounding computers areexpected to negatively influence computer use.Not surprisingly, people are expected to avoidbehaviors that invoke anxious feelings. A num-ber of studies have demonstrated a relationshipbetween computer anxiety and the use of com-puters (Igbaria, et al., 1989; Webster, et al.,1990).

H14: The higher the individual’s computeranxiety, the lower his/her use ofcomputers.

Research Design

SubjectsThe target population for the validation studywas knowledge workers--individuals whosework requires them to process large amounts ofinformation. This category includes most man-agers, as well as professionals such as insur-ance adjusters, financial analysts, researchers,consultants, and accountants. The subscriberlist of a Canadian business periodical was ob-tained as a sampling frame to reach this popula-tion. A recent readership survey of thisperiodical indicated that the subscribers wereprimarily employed in managerial positions, atall levels of the organization. Over 88 percenthave college or university degrees. Forty per-cent have graduate degrees (mostly MBAs).They represented all functional areas of busi-ness and a broad range of industries, includingmanufacturing, services, finance, communica-tions, advertising, government, oil and gas, andretailing. Eighty percent use a personal com-puter in their work. One hundred of these sub-scribers were randomly selected for the pilotstudy, and 2000 were randomly selected for themain study.

MeasuresIn addition to the self-efficacy measure de-scribed earlier, scales were required for each of

the other constructs in the model. A review ofthe literature was undertaken to identify con-struct definitions and any existing measures.Based on this review, scales were formed foreach of the constructs in the model. Previouslydeveloped and validated instruments wereadopted directly. A few constructs required ad-aptations of existing measures. Each of themeasures used in the study is described below.

Encouragement by Others. The extent towhich use of computers was encouraged byothers in the individual’s reference group wasmeasured by seven items. Respondents wereasked to assess, on a five-point scale, the ex-tent to which their use of computers was en-couraged by: (1) their peers in their workorganization, (2) their peers in other organiza-tions, (3) their family, (4) their friends, (5) manager, (6) other management, and (7) theirsubordinates.

Others’ Use. The extent to which computerswere actually used by others in the individual’sreference group was also assessed using sevenitems. Respondents were asked to indicate, ona five-point scale, the extent to which (1) theirpeers in their work organization, (2) their peersin other organizations, (3) their family, (4) theirfriends, (5) their manager, (6) other manage-ment, and (7) their subordinates actually usedcomputers.

Support. The organizational support for com-puter users was measured by six items, drawnfrom Thompson, et al. (1991). The respondentswere asked to indicate, on a five-point scale, theextent to which assistance was available interms of equipment selection, hardware difficul-ties, software difficulties, and specialized in-struction. They also rated (on the same scale)the extent to which their coworkers were asource of assistance in overcoming difficultiesand their perception of the organization’s overallsupport for computer users.

Outcome Expectations. An 11-item measureof outcome expectations was developed basedon a review of existing measures in the IS litera-ture. For example, Davis’ (1989) measure usefulness deals primarily with outcome expec-tations. Similarly, Pavri’s (1988) beliefs con-struct, and three of Thompson, et al.’s (1991)constructs reflect the expected consequences of

MIS Quarterly~June 1995 197


using a computer. The measure presented a va-riety of outcomes that might be associated withcomputer use, including increased productivity,decreased reliance on clerical support, enhancedquality of work output, feelings of accomplish-ment, and enhanced status. Respondents wereasked to indicate, on a five-point scale, howlikely they thought it was that each of these out-comes would result from their use of computers.

Affect. Affect was measured in this study by fiveitems, drawn from the Computer Attitude Scale(Loyd and Gressard, 1984). Respondents indi-cated, on a five-point scale, the extent to whichthey agreed or disagreed with items such as ’1like working with computers," and "Once I getworking on the computer, I find it hard to stop."

Anxiety. Anxiety was measured by the 19-itemComputer Anxiety Rating Scale (Heinssen, etal., 1987). This scale was found to be valid formeasuring computer anxiety (Webster, et al.,1990). Respondents indicated, on a five-pointscale, the extent to which they agreed or dis~agreed with statements such as "1 feel appre-hensive about using computers."

Use. Computer use was measured by fouritems, reflecting the duration and frequency ofuse of computers at work and the duration ofuse of computers at home on weekdays andweekends.

Procedures

A pretest of the questionnaire including all con-struct measures) was conducted with 40 people,including both academics and practitioners.Each of the respondents completed the question-naire and provided feedback about the processand the measures. Overall, the respondents in-dicated that the questionnaire was relativelyclear and easy to complete. A number of sug-gestions were made concerning the wording ofparticular items and the overall structure of thequestionnaire, and these suggestions were in-corporated into the revised instrument.

A pilot study involving 100 individuals from thesampling frame was also conducted. The pur-pose of the pilot study was (a) to gain additionalfeedback about the questionnaire instrumentand (b) to assist in determining the size of themailing for the main study. With respect to the

first objective, respondents were asked to pro-vide any comments on the questionnaire con-tent or structOre. Moreover, they were asked toindicate whether they would be willing to partici-pate in an interview with the authors. Five of therespondents were interviewed. During the inter-views, the respondents were probed about theirresponses to the different questionnaire items,in order to ensure that questionnaire responseswere consistent with other statements regardingcomputer attitudes and beliefs. With respect tothe second objective, determining sample sizefor the main study, the response rate was esti-mated and compared with the data require-ments for analyzing structural equationsmodels. For maximum flexibility in analyzing thedata, a minimum of 400 responses was consid-ered necessary (five times the number of vari-ables in the questionnaire). However, in order toprovide enough data for a holdback sample (sothat measurement revisions could be made withone set of data and then confirmed with theother), this figure was doubled. The responserate for the pilot study was 60 percent. Assum-ing a more conservative 50 percent responserate, at least 1600 surveys were required.

For the main study, 2,000 people were randomlyselected from the subscriber list. The surveywas mailed to the selected individuals with acover letter indicating the purpose and impor-tance of the study. In order to maximize the re-sponse rate, a stamped return envelope wasprovided with the survey, and a reminder letterwas sent to those individuals who had not re-sponded after three weeks. These procedureshave been found to aid in maximizing responsesto mail surveys (Dillman, 1978).

Responses

Of the 2,000 surveys mailed, 1,020 were com-pleted and returned, and 91 were re’turned asundeliverable. Thus, the response rate was 53.4percent. While this response rate is acceptablefor research of this nature, non-response biaswas a concern. Normally, non-response biaswould be assessed by contacting a sample ofnon-respondents and obtaining information fromthem about the survey measures, in order to as-sess whether they differed from respondents.Armstrong and Overton (1977), however, argue

¯ 198 MIS Quarterly/June 1995


that comparing early respondents to late re-spondents also provides information that can beused to predict non-response bias. Essentially,they claim that late respondents are likely to besimilar to non-respondents, since they requiredprompting to respond and were therefore pre-sumably less eager. Thus, if late respondentsand early respondents do not differ in theirscores, it is less likely that non-respondents willdiffer significantly from respondents. While thisis a less strong approach to assessing non-re-sponse bias, it does provide some indication.Using the early (first week) and late (after weeks) responses to the questionnaire, a multi-variate analysis of variance was undertaken todetermine whether differences in response time(early versus late) were associated with differentresponses. The test indicated no significant dif-ferences in any of the variables of interest(VVilks’ ,& = 0.97; p = 0.735).

The 1,020 respondents were mostly male (83percent) and had an average age of 41 years.They represented all levels of management andwere evenly split between line and staff posi-tions. They worked in a variety of functional ar-eas, including accounting and finance (18percent), general management (30 percent),and marketing (16 percent). Forty-three percenthad completed one college or University degree;a further 40 percent had completed post-gradu-ate degrees.The respondents’ educational back-grounds were primarily in business (61 percent),arts (10 percent), science (14 percent), and cial science (5 percent).

Data AnalysisAssessment of the research model was con-ducted using Partial Least Squares (PLS). PLSis a regression-based technique that can ana-lyze structural models with multiple-item con-structs and direct and indirect paths. PLSproduces Ioadings between items and con-structs (similar to principal components analy-sis) and standardized regression coefficientsbetween constructs. R2 values for dependentconstructs are also produced. The techniquehas gained acceptance in marketing and organ-izational behavior research and was first used in

the MIS literature by Rivard and Huff (1988) an examination of the factors of success forend-user computing. It has subsequently beenused by Grant and Higgins (1991) andThompson, et al. (1991) in studies of computer-ized performance monitoring and personal com-puter utilization, respectively. Barclay, et al. (inpress) provide a comprehensive description ofPLS analysis.

PLS analysis involves two stages: (a) assess-ment of the measurement model, including thereliability and discriminant validity of the meas-ures, and (b) assessment of the structuralmodel. For the assessment of the measurementmodel, individual item Ioadings and internal con-sistency reliabilities are examined as a test ofreliability. Individual item Ioadings and internalconsistencies greater than 0.7 are consideredadequate (Fornell and Larcker, 1981). For dis-criminant validity, items should load higher ontheir own construct than on other constructs inthe model, and the average variance shared be-tween the constructs and their measures shouldbe greater than the variances shared betweenthe constructs themselves.

The structural model and hypotheses are testedby examining the path coefficients (which arestandardized betas). In addition to the individualpath tests, the explained variance in the de-pendent constructs is assessed as an indicationof model fit. Finally, it is often useful to assessthe indirect effects and common cause effects,as in path analysis.

Initial measurement mode/The measures of four constructs (support, self-efficacy, affect, and use) satisfied the criteria forreliability and discriminant validity in the initialmodel. Thus, no changes to these constructswere indicated. The remaining constructs evi-denced some measurement problems. Theseproblems, and the associated revisions, are dis-cussed below.

Encouragement by Others. Three items in theencouragement-of-use construct did not corre-late highly with the other measures. Encourage-ment of use from family (X = 0.40), friends (X 0.51), and subordinates (X = 0.63) did not pear to correlate highly with encouragement ofuse from peers and managers.



Encouragement of use represents the influenceof social persuasion on self-efficacy and out-come expectations. The impact of this socialpersuasion depends on the perceived similarityof the individual as well as his or her credibility(Bandura, 1986). Thus, the influence of encour-agement is, in effect, moderated by the charac-teristics of the individuals doing theencouraging. In this case, the low Ioadings forencouragement of use from family, friends, andsubordinates may be viewed as reflecting arelative lack of attention to these sources of per-suasion. It would appear that, in forming judg-ments about their ability to use computers andabout the likely outcomes of using computers,these managers looked to their peers and theirsuperiors, but not to their subordinates or tofamily and friends. Thus, the latter three itemswere dropped from the model in subsequenttests.

Others’ Use. A similar problem was encoun-tered in the measures of actual use by others.Actual use by family (~. = 0.16), friends (Z 0.35), and subordinates (~. = 0.20) did not loadhighly on the construct. Moreover, actual use bysubordinates loaded more highly on the encour-agement-by-others construct (cross-loading 0.27) than on the others’-use construct. Thus, aswith the encouragement construct, these itemswere dropped from the subsequent analyses.

Outcome Expectations. Examination of theIoadings for the outcome expectations constructindicated the possibility of multiple underlying di-mensions for this construct. Loadings rangedfrom 0.42 to 0.83. Reconsideration of the itemsconfirmed this hypothesis. Two distinct dimen-sions appeared to be represented in the scale,corresponding to the job-related and other, morepersonal, outcomes of computer use. Job-re-lated outcomes included items such as "If I usea computer, I will increase the quality of outputof my job," while the personal outcomes in-cluded "If I use a computer, I will increase mysense of accomplishment."

For the revised model, then, the outcome ex-pectations construct was split into these two di-mensions. This necessitated a splitting ofhypotheses 2, 4, 6, 7, 11, and 12. In each case,the hypotheses were restated to include per-formance-related outcome expecations (H2a,H4a, H6a, H7a, H11a, H12a)and personal out-

come expectations (H2b, H4b, H6b, H7b, H11band H12b) specifically.

Anxiety. The individual item Ioadings for thisconstruct were poor, indicating a problem in themeasurement of anxiety. Re-examination of themeasure and exploratory factor analysis re-vealed a number of underlying dimensions. Rayand Minch (1990) also found this instrument be multi-dimensional and explained the resultsin terms of the construct of computer alienation.In our study, the sub-dimensions of the scale re-flected, in addition to anxiety, a desire to learnmore about computers, beliefs about learning touse computers, and beliefs about the appropri-ateness of computers in business and educa-tion. The latter three dimensions, while possiblyinfluencing the formation of anxiety, do not rep-resent anxiety itself. Thus, only the first dimen-sion was of interest in this study. Four itemsformed the core of this dimension in the factoranalysis. These items seemed to best capturethe feelings of anxiety associated with computeruse, and not the beliefs that might produce anxi-ety or other attitudes about computers. Thus,these four items were selected to represent theanxiety construct.

Revised model

The revisions to the measurement model weremade as indicated by the data, and the resultingmodel (Figure 3, shown with results) was testedusing the data from the holdback sample (n 481). The measurement statistics were substan-tially improved from the first model. The FactorStructure Matrix (Table 1) presents the individ-ual item Ioadings and cross Ioadings, andshows that the individual item Ioadings meet orexceed the 0.7 criteria except in 8 cases.2 Italso shows that none of the individual items loadmore highly on another construct than they doon the construct they were designed to meas-ure. The reliability and discriminant validity coef-ficients are reported in Table 2. All of the

2Eight items do not meet the 0.7 criteria. However. giventhat one set of revisions had already been done. and giventhat the overall statistics (Table 2) were adequate, it wasfelt that another set of measurement revisions would beinappropriate.



measures exceed 0.80 for internal consistencyreliability. In terms of discriminant validity, thevariance shared between each construct and itsmeasures (the diagonal elements in Table 2)exceeds the variance shared between each ofthe constructs (the off-diagonal elements). Thus,the measurement model tests were adequate,indicating that the revisions to the measuresachieved the desired effects.

Once the measurement model was consideredacceptable, the path coefficients were as-sessed. All but four of the paths provided suP-port for the study’s hypotheses. The relationshipbetween others’ use of computers and personaloutcome expectations, while in the direction pre-dicted by the model, was not significant. More-over, contrary to the hypotheses, support wasnegatively related to self-efficacy (H5) and both performance-related (H6a) and personaloutcome expectations (H6b).

While all but one of the paths were statisticallysignificant (p < 0.01), the substantive signifi-cance (or effect magnitudes) of the relationshipsmust also be considered in the assessment ofthe model. The path coefficients in the PLSmodel represent standardized regression coeffi-

cients. The suggested lower limit of substantivesignificance for regression coefficients is 0.05(Pedhazur; 1982). As a more conservative posi-tion, path coefficients of 0.10 and above arepreferable. Thus, the path from Personal Out-come Expectations to Usage (!3 = 0.03), whilestatistically significant (p < 0.01), is not consid-.ered substantively significant.

The path coefficients represent the direct effectsof each of the antecedent constructs. It is alsoimportant, however, to consider the total effects,including those that are mediated by other vari-ables. Performance-related outcome expecta-tions and self-efficacy have roughly equal directeffects on use. However, when the total effectsare considered, self-efficacy emerges as a morepowerful predictor (total effect = 0.423 versus0.269 for outcome expectations), since it also in-fluences use indirectly through outcome expec-tations, affect, and anxiety. Table 3 presents thedirect effects, indirect effects, and spurious orunexplained correlations between each of thevariables in the model.In total, the model explained 37 percent of thevariance in affect, 25 percent of the variance inanxiety, and 32 percent of the variance in use.

0.12"** 0.225***

Encouragementby Others

0.20***

Others’ Use

0.015

Support

0 *** Affect

ComputerSelf-Efficacy X-0.so ~ / o.10"*’

" / .... /\1 AnxietyOutcome / "1

Expectations--

-0.16"** Perf°rmanc-~e~ii* Us!£ "le 1"**

Outcome 0.03*** ’1 " ’Expectationsm

Personal

*** p< 0.01.

Figure 3. Revised Model and Path coefficients

MIS Qua~efly/June 1995 201


Table 1. Factor Structure Matrix-Revised Model1 2 3 4 5 6 7 8 9

789101112131415161718192021222324252627282930313233343536373839404142434445464748

5 0.4786 0.371

0.4380.4150.1650.1700.1850.1890.1660.2710.1280.1330.1630.1780.1450.2220.1450.1240.1740.2070.1680.2330.2420.2100.2040.1120.1180.0730.1910.0810.2280.1800.1410.1140.1420.166

-0.104-0.056-0.085-0.0910.2020.1840.0180.013

0.4910.3400.4350.397

o.11o0.0950.0900.1250.1680.2060.0830.0920.1990.1540.1270.1660.1350.1500.1600.1540.0550.1720.1680.2460.2070.0780.0580.0220.1530.0180.1280.1550.0860.0620.1050.145

-0.072-0.013-0.075-0.0570.2610.2440.0490.074

0.2390.0540.2200.2730.2160.0460.2230.330

-0.156-0.077’,-0.109-0.086-O.054-0.037-0.078-O.037’-0.062-0.071,-0.073-0.037-O.054-0.124-0.1230.042

-0.089-0.064-0.035-0.177-0.124’-0.121-0.124-0.151-0.060-0.045-0.003. 0.015-0.001-O.004-0.0650.046

-0.083. -0.076

0.1560.1420.1900.1430.1780.1520.033

-0.022-0.046-0.013-0.004-0.063-0.147-0.074

(0.1120.2970.1670.2920.2930.1400.1080.0670.1700.1230.1730.4610.3750.2180.4410.303

-0.430-0.304-0.347-0.4670.3810.3610.2790.254

0.225 0.1330.221 0.1470.258 0.1800.144 0.1530.198 0.1190.208 0.0870.091 0.0270.025 0.013

-0.095 -0.100-0.039 -0.028-0.041 -0.064-0.066 -0.113-0.088 -0.108-0.073 -0.1020.263 0.1100.232 0.1250.262 0.1230.269 0.1710.246 0.0980.237 0.1790.273 0.1190.243 0.1410.228 0.1560.298 0.199

0.3390.428

- 0.3380.4190.3280.234

0.3390.4090.4130.296:0~0.3760.455 0.2800.431 0.3420.292 0.2510.290 0.1130.326 0.227

-0.235 -0.075-0.088 0.052-0.159 -0.014-0.225 -0.0780.374 0.2360.377 0.2190.187 0.1230.207 0.066

0.135 -0.089 0.1140.188 -0.112 0.0650.182 -0.087 0.2190.119 -0.048 0.1150.154 -0.070 0.2330.133 -0.094 0.1760.019 0.068 0.156

-0.050 0.064 0.097-0.089 0.003 -0.075-0.051 -0.031 0.016-0.023 -0.038 0.044-0.102 0.007 -0.006-0.166 0.024 -0.105-0.084 -0.012 -0.0070.400 -0.402 0.3380.393 -0.378 0.3230.451 -0.497 0.4110.440 -0.405 0.3800.419 -0.452 0.3750.395 -0.344 0.3250.400 -0.399 0.4080.279 -0.323 0.3310.352 -0.356 0.2860.423 -0.435 0.4110.335 -0.108 0.2270.456 -0.252 0.4060.275 -0.137 0.2240.405 -0.168 0.3400.365 -0.168 0.3110.201 -0.153 0.2450.232 -0.083 0.1840.273 -0.048 0.1390.286 -0.089 0.2450.184 0.045 0.1040.232 0.015 0.196

-0.454 0.437-0.284 0.349-0.191 0.208-0.602 0.392-0.334 0.339

-0.486 -0.344-0.264 -0.244-0.364 -0.254-0.463 -0.3190.415 -0.2920.386 -0.3340.266 -0.2270.266 -0.202

Constructs: 1. Encouragement of Use2. Others’ Use3. Support4. Computer Self-Efficacy5. Performance Outcome Expectations

6. Personal Outcome Expectations7. Effect8. Anxiety9. Use



Table 2. Reliability and Discriminant Validity Co-Efficients--Revised Model

Construct ICR§ 1 2 3 4 5 6 7 8 91. Encouragement 0.87 0.802. Others’ Use 0.80 0.52* 0.723. Support 0.91 0.24* 0.18" 0.794. Self-Efficacy 0.95 0.20* 0.18" -0.10"* 0.815. Outcome Exp.--Performance 0.87 0.27* 0.22* -0.09** 0.32* 0.726. Outcome Exp.--Other 0.87 0.19" 0.11"* -0.12" 0.17" 0.49*7. Affect 0.87 0.20* 0.15" -0.13" 0.49* 0.48*8. Anxiety 0.87 -0.11"* -0.07 -0.00 -0,50* -0.23*9. Use 0.82 0.17" 0.24* -0.05 0.45* 0.41"

0.760.32* 0.75

-0.05 -0.51" 0.790.24* 0.47* -0.37* 0.73

Note: Diagonal elements are the square root of the variance shared between the constructs and their measures. Off-diagonal elementsare the correlations among constructs. For discriminant vallidity, diagonal elements should be larger than off-diagonal elements.*p < 0.01; ** p < 0.05.§ Internal Consistency Reliabilty.

In addition, 7 percent of the variance in self-effi-cacy, 17 percent of the variance in performance-related outcome expectations, and 8 percentof the variance in personal outcome expec-tations was explained. Since the objective ofthis study was primarily the understandingof behavioral outcomes (rather than predic-tion of self-efficacy), the model was accept-able in terms of explanatory power.

DiscussionThe findings of this study provide support for theresearch model and the Social Cognitive Theoryperspective on computing behavior. Self-effi-cacy was found to play an important role inshaping individuals’ feelings and behaviors. Indi-viduals in this study with high self-efficacy usedcomputers more, derived more enjoyment fromtheir use, and experienced less computer anxi-ety. In addition, outcome expectations, in par-ticular those relating to job performance, werefound to have a significant impact on affect andcomputer use. Affect and anxiety also had a sig-nificant impact on computer use.

The analysis also sheds light on the mediatingrole of self-efficacy and outcome expectations inthe processing of environmental information.Several studies have demonstrated the influ-ence of encouragement of use on computingbehavior (e.g., Higgins, et al., 1990; Pavd,1988). This study is consistent with those find-ings, but it also suggests the mechanismsthrough which encouragement by others oper-

ates. Previous research posited a direct rela-tionship between encouragement and use. Thisresearch suggests that encouragement influ-ences behavior indirectly, through its influenceon self-efficacy and outcome expectations.Similarly, others’ actual use of computers influ-ences behavior through its influence on self-effi-cacy and outcome expectations.

A surprising, and somewhat puzzling, findingwas the negative influence of support on self-ef-ficacy and outcome expectations. From a theo-retical perspective, it seemed logical to hypothe-size that higher organizational support would re-sult in higher judgments of self-efficacy on thepart of individuals because they would havemore resources to help them become more pro-ficient. Moreover, support was believed to be anindication of organizational norms regardinguse, and would thus positively influence out-come expectations in addition to self-efficacy.However, the data analysis suggests a negativerelationship.

The reasons for the~e findings are not entirelyclear, but several possibilities exist. With respectto self-efficacy in particular, it may be that indi-viduals with lower self-efficacy are more awareof the existence of support within their organiza-tions than those with high self-efficacy becausethey make more use of those systems. This ex-planation would indicate that the causal arrowsbetween self-efficacy and support should be re-versed. Alternatively, the presence of high sup-port may in some ways actually hinder theformation of high self-efficacy judgments. If indi-viduals can always call someone to help themwhen they encounter difficulties, they may never

MIS Quarter/y/June 1995 203


be forced to sort things out for themselves andthus may continue to believe themselves inca-pable of doing so. These alternative explana-tions have very different implications fororganizations, and the data provide no indica-tion as to which might be correct. Thus, addi-tional research is needed to investigate thisfinding.

From a measurement standpoint, the dataanalysis provides evidence of the construct va-lidity of the computer self-efficacy measure. Thescale demonstrated high internal consistency(reliability), empirical distinctness (discriminantvalidity), and was related as predicted to theother constructs (nomological validity). Thus,

based on this evidence, it appears to be usefulas a measure of computer self-efficacy.

The development and validation of a measure ofcomputer self-efficacy represents an importantstep in the development of theories about self-efficacy and computer use. Zmud and Boynton(1989) argue that too little attention has beenpaid to measure development in informationsystems research and that theoretical advance-ment has been constrained by the absence ofvalidated measures. This research answerstheir call for careful development of measuresand provides a valid measure of computer self-efficacy that can be used in a variety of researchsettings.

Table 3. Indirect and Spurious Effects

RelationshipEncouragement by Others--Others’ UseEncouragement by Others---SupportEncouragement by Others--Self-EfficacyEncouragement by Others--Performance Outcome Exp.Encouragement by Others--Personal Outcome Exp.

DirectEffect

0.180.200.20

IndirectEffect

n.a.n.a.0.050.02

Spurious andUnexplainedCorrelation

0.520.240.020.02

-0.03Encouragement by Others--AffectEncouragement by Others--AnxietyEncouragement by OthersoUseOthers’ Use--SupportOthers’ Use~Self-EfficacyOthers’ Use--Performance Outcome ExpectationsOthers’ Use--Personal Outcome ExpectationsOthers’ Use--AffectOthers’ Use--AnxietyOthers’ Use--UseSupport--Self-EfficacySupport--Performance Outcome ExpectationsSupport--Personal Outcome ExpectationsSupport--AffectSupport--AnxietySupport--UseSelf-Efficacy--Performance Outcome ExpectationsSelf-Efficacy--Personal Outcome ExpectationsSerf-Efficacy--AffectSelf-Efficacy--AnxietySelf-Efficacy--Use

n.a.n.a.n.a.0.110.100,015

-0.16-0.14-0.16

n.a.

rl.a.

n.a,

0.240.120.37

-0.500.225

0.17-0.090.14

0.030.0150.08

-0.050.07

-0.04-0.02-0.130.08

-0.11n.a.

R.a.

0.09n.a.

0,20

0.03-0.020.030.180.070.090.080.07

-0.020.170.060.090.060-0.080.060.070.060.030n.a.

Performance Outcome Expectations--Personal Outcome ExpPerformance Outcome Expectations---AffectPerformance Outcome Expectations~AnxietyPerformance Outcome Expectations--UsePersonal Outcome Expectations--AffectPersonal Outcome Expectations--AnxietyPersonal Outcome Expectations --UseAffect--AnxietyAffect--UseAnxiety--Use

noa.

0.32n.a.0.210.10B.a.

0.03

0.19-0.11

noa.

n.a.n.a.0.06n.a.n.a.0.02n.a.n.a.n.a.

0.490.17

-0.230.140.21-0.050o19-0.510.28-0.26

204 MIS Quarterly/June 1995


Of course, the validity of a measure cannot betruly established on the basis of a single study.Validation of measures is an ongoing process,which requires the assessment of measurementproperties over a variety of studies in similar anddifferent contexts. Moreover, a comparison ofthis measure of computer self-efficacy with othermethods of capturing the same construct wasnot conducted in this study. The absence of atest of convergent validity detracts to a degreefrom the conclusions drawn on the basis ofthese data. However, questionnaire measuresare the standard format for measuring self-effi-cacy, and other methods upon which to drawwere not available. Thus, this study representsthe most comprehensive assessment of validitythat could be made based on existing knowl-edge. The development of other means of as-sessing computer self-efficacy may be consid-ered as an opportunity for future research.

Limitations

These findings must be considered in light of thestudy’s limitations, in particular the use of cross-sectional survey data. Social Cognitive Theorypredicts causal relationships between the con-structs studied. PLS analysis, like other struc-tural equations modeling, provides strongsupport for this interpretation relative to othertechniques such as correlation and regressionsince all of the relationships (including those inthe measurement model as well as in the struc-tural model) are tested simultaneously. How-ever, conclusive statements about causalitycannot be made since alternative explanationscannot be ruled out. Moreover, Social CognitiveTheory is based on a continuous reciprocal in-teraction among the factors studied. Feedbackmechanisms could not be modeled with thepresent data, and thus the model tested is in-complete. Further research, in particular experi-mental and longitudinal studies, are clearlyneeded to address these issues.

O.ne limitation with respect to the self-efficacymeasure is the use of a hypothetical scenariofor responses. In answering the self-efficacyscale the respondent is asked to imagine he orshe has been given a new software package foruse in his or her job. The type of software pack-

age is not specified and is indicated as unimpor-tant. The purpose of this approach was to forcerespondents to think about future behaviorrather than past capability, and to think aboutgenerating novel responses rather than fixedpatterns (Bandura, 1986). However, this ap-proach raises two problems. First, does the hy-pothetical scenario represent actual situations?That is, are respondents capable of imaginingall that is required of them to answer the items?This question was posed to respondents in thepretest and pilot studies. None of the respon-dents indicated any difficulty in answering thequestions, and when probed with respect totheir confidence in being able to use computersgenerally, the responses were consistent withthe questionnaire responses. The second con-cern relates to self-efficacy with respect to learn-ing versus using computers. By focusing on anunfamiliar software package, the notion of self-efficacy with respect to/earning to use comput-ers is introduced as an additional dimension ofthe construct. VVhile the ability to adapt to newtechnology is a fundamental part of being acompetent computer user, it is possible that self-efficacy with respect to learning is different thanself-efficacy with respect to using computers.

Implications for research andpractice

In spite of the above noted limitations, thesefindings demonstrate the value of Social Cogni-tive Theory. IS research to date has generallynot considered how individuals’ expectations oftheir capabilities influence their behavior andthus paints an incomplete picture. It suggeststhat individuals will use computing technology ifthey believe it will have positive outcomes. So-cial Cognitive Theory, on the other hand, ac-knowledges that beliefs about outcomes maynot be sufficient to influence behavior if individu-als doubt their capabilities to successfully usethe technologies. Thus, the Social CognitiveTheory perspective suggests that an under-standing of both self-efficacy and outcome ex-pectations is necessary to understand comput-ing behavior. This research, in developing andtesting a measure of computer self-efficacy, laysthe foundation for future research concerningthe Social Cognitive Theory perspective on


Computer Self-EfficacymMeasurement

computing behavior and the umque influenceof individuals’ perceptions of their computingabilities.

Future research is necessary to address thelimitations of this research. First, longitudinalevidence is required. This research relied oncross-sectional data, making interpretation ofcausality problematic. Second, additional de-pendent variables need to be studied. Thisstudy focused on self-reports of computer use.Self-efficacy, however, is also argued to influ-ence the development of ability. Thus, futureresearch should focus on how computer self-efficacy influences the development of comput-ing skill. The generalizability of computer self-ef-ficacy also warrants research attention. Thisstudy focuses on self-efficacy with respect tocomputers in general. Bandura (1986) arguesthe need to tailor self-efficacy measures to thedomain of interest in order to maximize predic-tion. It is not clear, however, what constitutesthe domain of interest with respect to comput-ers. To what extent are computer self-efficacyperceptions with respect to specific software orhardware domains correlated? Is it reasonableto use general self-efficacy measures, or is itnecessary to tailor the items to these specifichardware and/or software domains? Further-more, the relationship between learning and us-ing computers needs to be addressed. Thismeasure of self-efficacy, using a hypotheticalsoftware package, incorporates an individual’sconfidence in his or her ability to learn to usecomputer software packages, in addition to theability to use the package. Future researchshould investigate the extent to which theseconstructs are related.

From a managerial standpoint, these findingssuggest the importance of understanding indi-viduals’ self-perceptions and finding ways inwhich to address them. For example, one of theinterpretations of the negative relationship be-tween organizational support and self-efficacy isthat the way in which technical support is pro-vided actually hinders the development of self-efficacy. When faced with a computer problemthat he or she cannot resolve, a user will oftencall a technical support person for assistance. Ifthe support person, as the authors’ experiencesuggests they sometimes do, dashes into the of-rice, sits down at the users’ chair, bangs away at

the keyboard for a few minutes and then pro-claims that the problem is fixed, it would not besurprising that the user would begin to doubt hisor her capabilities. While this explanation wouldaccount for the negative relationship betweensupport and self-efficacy, it is not the only onepossible. If it is true, however, it represents aproblem for providing support. It suggests thatsupport personnel must also attend to self-effi-cacy concerns, perhaps by explaining to theuser what they are doing, suggesting reasonsfor why the problem arose, and giving informa-tion about what to do if the problem ever occursagain.

In a broader sense, organizations need to beaware of the concept of self-efficacy and themeans for encouraging it. Bandura (1986) de-fines four sources of self-efficacy information:guided mastery, behavior modeling, social per-suasion, and physiological states. The strongestsource of information is guided mastery--actualexperiences of success in dealing with the be-havior. The more successful interactions indi-viduals have with computers~ the more likelythey are to develop high self-efficacy. This hasstrong implications with respect to training. First,it suggests that hands-on practice is a key com-ponent of training, so that people can build theirconfidence along with their skill. More impor-tantly, however, the need for successful experi-ence in order to foster self-efficacy is a strongargument for why software training is so impor-tant to new users. If users are working with anew software package without adequate train-ing, they are likely to experience problems. Be-cause they are struggling, they may actuallylower their sense of self-efficacy and becomereluctant to use the technology, thus defeatingthe purpose of introducing new technology.

The second source of self-efficacy information isbehavior modeling, which involves observingsomeone else performing the behavior as ameans of learning. Compeau and Higgins (inpress) demonstrate that a behavior modelingapproach to computer training can enhanceself-efficacy perceptions and performance in thetraining context. Social persuasion also exertsan influence on self-efficacy. Reassurance tousers that they are capable of mastering thetechnology and using it successfully can helpthem to build confidence.

206 MIS Quarter~y/June 1995


Finally, physiological states, specifically feelingsof anxiety, can have a lowering effect on self-ef-ficacy. Bandura (1986) argues that individualssometimes interpret their feelings of anxiety to alack of ability. Thus, if an individual feels anx-ious when using a computer, he or she may de-cide that the reason for the feelings of anxiety isa lack of ability, thus lowering his or her self-effi-cacy. Webster, et al. (1990) found that computeranxiety in the training process can be reducedby encouraging playful behavior. Thus, theremay be indirect ways of influencing physiologi-cal states, which can lessen their negative im-pact on self-efficacy.

Concluding CommentsThis research has attempted to demonstrate theutility of the self-efficacy construct, borrowedfrom social psychology, to understand individualcomputing behavior. The results indicate thatself-efficacy adds to our understanding of whypeople use computers, over and above con-cepts like outcome expectations, anxiety, andaffect. In addition, the research has provided areliable measure that satisfies the major condi-tions for construct validity (discriminant and no-mological validity). Future research should focuson examining the impact of self-efficacy on de-velopment of computer skills and on under-standing the generalizability of computerself-efficacy.

AcknowledgementsThis research was supported by grants fromthe Social Science and Humanities ResearchCouncil of Canada and a doctoral fellowship fromthe National Centre for Management Researchand Development, The University of WesternOntario.

We are deeply indebted to Colette Frayne forher assistance in developing the research, andto the anonymous reviewers for their insightfulcomments.

Earlier versions of this paper were presented atthe Administrative Sciences Association of Can-ada 1991 conference (May 30 - June 2; NiagaraFalls) and at the 1991 International Conference

on Information Systems (Dec. 16-19, New York,NY).

Please direct all correspondence concerningthis article to Debbie Compeau, School of Busi-ness, Room 1204 Dunton Tower, Carleton Univer-sity, Ottawa, Ontario, K1S 5B6 (613-788-3993).

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About the AuthorsDeborah R. Compeau is an assistant professorof information systems at the School of Busi-ness, Carleton University. She received herPh.D. from the University of Western Ontado in1992. Her research interests include the designand delivery of effective end-user training, indi-vidual factors that influence end-user learningand adoption of information systems, and organ-

izational adoption of information systems. Herwork has been presented at the InternationalConference on Information Systems.

Christopher A. Higgins is an associate profes-sor in the Information Systems group at theWestern Business School, The University ofWestern Ontario. Professor Higgins has publishedover 40 articles in journals including: Communi-cations of the ACM, Administrative ScienceQuarterly, Information Systems Research, MISQuarterly, Journal of Applied Psychology, SloanManagement Review, and ACM Transactionson Office Information Systems. He has also co-authored two books. Specific research interestsinclude computerized monitoring, organizationalchampions, and telework. Two of Dr. Higgins’Ph.D. students have won international thesiscompetitions for their research. Professor Hig-gins is currently the co-holder of the ImperialLife Professor Chair.

Appendix

Computer Self-Efficacy MeasureOften in our jobs we are told about software packages that are available to make work easier. For thefollowing questions, imagine that you were given a new software package for some aspect of your work.It doesn’t matter specifically what this software package does, only that it is intended to make your jobeasier and that you have never used it before.

The following questions ask you to indicate whether you could use this unfamiliar software package un-der a variety of conditions. For each of the conditions, please indicate whether you think you would beable to complete the job using the software package. Then, for each condition that you answered "yes,"please rate your confidence about your first judgment, by circling a number from 1 to 10, where 1 indi-cates "Not at all confident," 5 indicates "Moderately confident," and 10 indicates "Totally confident."

For example, consider the following sample item:

I COULD COMPLETE THE JOB USING THE SOFTWARE PACKAGE...

...if there was someone giving me step by stepinstructions.

NOTATALL MODERATELY TOTALLY

CONFIDENT CONFIDENT CONFIDENT

~ ....1 2

NO

3 4(~ 6 7 8 910



The sample response shows that the individual felt he or she could complete the job using the softwarewith step by step instructions (YES is circled), and was moderately confident that he or she could do (5 is circled).

I COULD COMPLETE THE JOB USING THE SOFTWARE PACKAGE...

Q-1 .... if there was no one around to tell me what to doas I go.

Q-2 .... if I had never used a package like it before.

Q-3 .... if I had only the software manuals for reference.

Q-4 .... if I had seen someone else using it before tryingit myself.

Q-5 .... if I could call someone for help if I got stuck.

Q-6 .... if someone else had helped me get started.

NOT ATALL MODERATELY TOTALLY

CONFIDENT CONFIDENT CONFIDENT

I----I I~1 I~1YES ........ 1 2 3 4 5 6 7 8 9 10

NO

YES ........ 1 2 3 4 5 6 7 8 9 10

NO

YES ........ 1 2 3 4 5 6 7 8 9 10

NO

YES ........ 1 2 3 4 5 6 7 8 9 10

NO

YES ........ 1 2 3 4 5 6 7 8 9 10

NO

YES ........ 1 2 3 4 5 6 7 8 9 10

NO

Q-7 .... if l had a lot of time to complete the job for which the YES ........ 1 2 3 4 5 6 7 8 9 10software was provided.

Q-8 .... if I had just the built-in help facility for assistance.

Q-9 .... if someone showed me how to do it first.

NO

YES ........ 1 2 3 4 5 6 7 8 9 10

NO

YES ........ 1 2 3 4 5 6 7 8 9 10

NO

YES ........ 1 2 3 4 5 6 7 8 9 10

NO

Q-IO. if I had used similar packages before this one to dothe same job.

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