The Effects of Self-efficacy on Computer Usage - CiteSeerX

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Omega, Int. J. Mgmt Sci. Vol. 23, No. 6, pp. 587-605, 1995 Copyright © 1995 ElsevierScienceLtd 0305-0483(95)00035-6 Printed in Great Britain.All rights reserved 0305-0483/95 $9.50 + 0.00

The Effects of Self-efficacy on Computer Usage M IGBARIA Claremont Graduate School, Claremont, Calif., USA J IIVARI University of Jyvaskyla, Jyvaskyla, Finland

(Received January 1995," accepted after revision June 1995) This paper examines the effect of self-efficacy, belief in one's capabilities of using a computer in the accomplishment of specific tasks, on computer usage. It introduces an extended technology acceptance model (TAM) that explicitly incorporates self-efficacy and its determinants (experience and organizational support) as factors affectingcomputer anxiety, perceivedease of use, perceivedusefulness and the use of computer technology. A survey of 450 microcomputer users in Finland found strong support for the conceptual model. In accordance with TAM, perceived usefulness had a strong direct effect 6n usage, while perceived ease of use had indirect effect on usage through perceived usefulness. Self-efficacyhad both direct and indirect effects on usage, demonstrating its importance in the decision to use computer technology. It also had a strong direct effect on perceivedease of use, hut only an indirect effect on perceived usefulness through perceived ease of use. Computer experience was found to have a strong positive direct effect on self-efficacy, perceived ease of use, perceived usefulness and usage. Organizational support and computer anxiety had only indirect effects on usage, mainly through perceived usefulness. Implications of these findings are discussed for researchers and practitioners.

Key words--self-efficacy, acceptance, usage, Finland, globalization, cultural differences.

INFORMATION TECHNOLOGY (IT) with its c a p a c i t y to process, store a n d t r a n s m i t inform a t i o n has a significant p o t e n t i a l i m p a c t on o r g a n i z a t i o n a l effectiveness a n d p r o d u c t i v i t y [13, 17, 31, 32, 64, 87]. D e s p i t e the realization that I T is key to the success a n d survival o f c o m p a n i e s in a highly c o m p e t i t i v e e n v i r o n m e n t , the p o t e n t i a l benefits o f c o m p u t e r s as aids to m a n a g e r i a l decision m a k i n g m a y n o t be fully realized due to p o o r a c c e p t a n c e by users [88]. I n d i v i d u a l s are s o m e t i m e s unwilling to accept a n d use available systems a n d express less t h a n enthusiastic response to new t e c h n o l o g y introd u c e d by c o m p a n i e s , even if the system m a y increase their p r o d u c t i v i t y [1 l, 102]. A Fortune article states t h a t " M a n y w o r k e r s are suspicious o f new t e c h n o l o g y , even hostile to it" [32] (p. 44).

The a c c e p t a n c e a n d use o f c o m p u t e r s by i n d i v i d u a l s a p p e a r to be limited due to fear o f c o m p u t e r s , confidence a n d ability, resistance to new technology, perceived difficulty o f use, not u n d e r s t a n d i n g the i m p o r t a n c e o f technology, a n d lack o f m o t i v a t i o n to a d o p t a new t e c h n o l o g y [20, 45, 91]. G r e a t e r a t t e n t i o n needs to be p a i d to the factors that cause i n d i v i d u a l resistance to c o m p u t e r usage. IS research has a t t e m p t e d to identify n u m e r ous factors affecting computer usage [20, 29, 30, 51, 63, 104]. A n i n d i v i d u a l ' s perceived ability to a d o p t c o m p u t e r t e c h n o l o g y successfully has been shown to be a m a j o r factor affecting his o r her willingness to accept new t e c h n o l o g y [23,45, 59]. A m o n g the various theoretical m o d e l s d e v e l o p e d to examine individ587

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Igbaria, livari--Effects of Self-efficacy on Computer Usage

ual reactions to computing technology, the technology acceptance model (TAM) of Davis et al. [20] has emerged as especially promising [1, 69]. Although research on TAM has provided insights into computer usage, it has focused on perceived ease of use and perceived usefulness as the determinants of usage rather than on the external factors affecting these determinants. Additionally, TAM suggests that individuals will use computer technology if they believe it will result in positive outcomes. It does not explicitly consider how individuals' expectations of their capabilities influence their behavior. On the other hand, Social Cognitive Theory claims that beliefs about outcomes may be insufficient to influence behavior if individuals doubt their capabilities to successfully undertake behaviors [6-8]. Bandura [6] argues that self-efficacy, in addition to outcome expectations, must be considered. He states that "individuals can believe that a particular course of action will produce certain outcomes, but if individuals entertain serious doubts about whether they can perform the necessary activities such information does not influence their behavior" [6] (p. 193). This argument emphasizes the impact of the individual's cognitive state on outcomes and the importance of understanding both self-efficacy and outcome expectations. The perceived usefulness construct measured by Davis [19] and Davis et al. [20] reflects beliefs (or expectations) about outcomes. Self-efficacy, the belief that one has the ability to perform a particular action, is an important construct of the Social Cognitive Theory. Therefore, this research is aimed at investigating the role of both outcome expectations and self-efficacy in computing behavior. Self-efficacy has been shown to be associated with an individual's performance in computer training and technology acceptance [12, 15, 16, 23, 38, 45, 67, 98]. Studies have found evidence of a relationship between self-efficacy and (a) registration in computer courses at university [45], (b) adoption of high technology products [44], (c) innovation [12], and (d) performance in software training [16, 38, 98]. Given the importance of self-efficacy for predicting and improving work performance and behavior [7, 8, 36], these studies argued the need for further research to examine fully the role of self-efficacy in computing behavior. Furthermore, prior research on TAM is confined to North American samples. The present study extends TAM by

incorporating the external factors affecting computer usage and by testing the model in a a European country. Specifically, it seeks to extend previous research by incorporating self-efficacy and its determinants (experience and organizational support) as the external factors affecting computer anxiety, perceived ease of use, perceived usefulness and the usage of computer technology. It tests the model using a survey targeted to professional and managerial users of computers in Finland. RESEARCH BACKGROUND

Social Cognitive Theory and self-e~icacy

The present study is influenced by the Social Cognitive Theory (SCT) which is a widely accepted and empirically validated theory of individual behavior based on the work of Bandura [6-8]. SCT incorporates two specific expectations: (1) outcome expectations; and (2) expectations related to self-efficacy. Outcome expectations are similar to the perceived usefulness construct developed by Davis [19], where individuals tend to undertake behaviors they believe will help them perform their job better. Wood and Bandura state that "selfefficacy refers to beliefs in one's capabilities to mobilize the motivation, cognitive resources, and courses of action needed to meet given situational demands" [101] (p. 408). SCT claims that both expectations are basic determinants of user behavior. Bandura [6-8] suggests that perceived self-efficacy plays an important role in affecting motivation and behavior. The individuals' perceived abilities to attain the standards they have been pursuing have an impact on individual cognitive and behavioral reactions (i.e. motivation and performance). Those individuals who distrust their capabilities are easily discouraged by failure, whereas those who are highly assured of their efficacy for goal attainment will intensify their efforts when their performances fall short and persevere until they succeed. Bandura also identified several sources of information about self-efficacy expectations, among them are enactive mastery (personal experience) and verbal persuasion (e.g. perceived encouragement and support from others). Despite the acceptance of SCT within the psychological and organizational behavior literature, and given its importance for predicting

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intentions [20]. The importance of subjective norms can be assumed to be related to an individualism-collectivism dimension in Hofstede's framework. This leads to an interesting question as to what extent the TAM may be valid in more collective societies than USA. Therefore, this study examines the determinants of computer technology acceptance in Finland, Cultural context and external validity which differs culturally from North America in A central concern in scientific research is several respects. In his analysis of 50 countries external validity and "a key dimension of and 3 regions, Hofstede [48] listed the rank external validity is international" [86] (p. 28). numbers of the countries from high to low (I for Hofstede [47-49] recognizes that many popular the largest, 53 for the smallest) and found that management and motivation theories such as both USA and Finland have small power Herzberg's two-factor theory [43], Maslow's distances (scores 40 and 33, and ranks 38 and 46, hierarchy of needs [68] and McGregor's theories respectively), but differ in individualism (scores [70] reflect the North American culture and 91 and 63, and ranks 1 and 17, respectively), argues that their applicability in other cultures is uncertainty avoidance (scores 46 and 59, and questionable. A rudimentary survey of IS ranks 43 and 31/32, respectively) and especially research indicates that almost all existing masculinity (scores 62 and 26, and ranks 15 and empirical IS research has been conducted in 47, respectively). Because the empirical analysis North America using American subjects. The of the long-term orientation of 23 countries did applicability of this body of IS research findings not include Finland, it is omitted here. Lachman et al. [56] propose that any to other cultures is unknown. The European Journal of i n formation Systems claimed that "the comprehensive cross-national research on orsocial and cultural characteristics of European ganizations and management should incorporate institutions can be studies as distinct from, or resource availability since resources affect perhaps in contrast to, North American or organizational structure and behavior. AmeriJapanese institutions" [60] (p. 1); it also cans have more access to new technology suggested that research addressing European (particularly microcomputers) than other users. concerns should be conducted. Additionally, The Scandinavian countries (Denmark, Finland, Aharoni and Burton [2] in a special issue of Norway and Sweden) form an interesting Management Science suggested that more comparison to the United States because IT research is needed to address the generalizability spending per capita is high. According to the of management science, where our knowledge in recent OECD report concerning the year 1989 many ways is specific and limited to a given [77] they, together with Switzerland, occupied the country or a culture. They concluded that since first five positions in per capita IT spending, with "[T]he world has increasingly become a global the United States being sixth. Finland is village, and large, multinational enterprises particularly interesting from the viewpoint of the operate in a globally integrated fashion" (p. 1), present paper because the proportion of we need to examine whether the findings of spending on PCs and work stations was clearly studies conducted in one region, mainly, North the highest among the OECD countries. America, are valid in other cultures. Despite these concerns, cross-cultural studies in the IS CONCEPTUAL MODEL AND RESEARCH field are quite rare [85]. HYPOTHESES Hofstede argues in several contexts that popular motivation theories are culture-bound. In addition to the SCT [6-8], the theoretical As will be explained below, TAM is derived from grounding for this research comes from Fishbein the theory of reasoned action (TRA) [24]. A and Ajzen's [24] theory of reasoned action conspicuous difference between the two is that (TRA), Ajzen's [3, 4] theory of planned behavior TAM omits subjective norms, mostly because of (TPB); and Davis et al. [20] technology methodological reasons and partly because they acceptance model (TAM). SCT provides a were not significant in explaining behavioral theoretical basis for describing behavioral and

and improving work performance and behavior [7,8,15,16,36], it has rarely been used within the IS context. While outcome expectations have been researched by IS researchers [1, 19, 20, 51, 91], more research is needed to explore fully the role of self-efficacy in computing behavior.

Igbaria, Iivari--Effects of Self-efficacy on Computer Usage

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affective reactions to computing technology. This theory posits that expectations are the primary determinants of behavior and affective outcomes. Specifically, it states that environmental and personal factors such as verbal (i.e. social) persuasion and experience influence expectations that subsequently affect individuals' outcomes. TAM adapted the generic Fishbein and Ajzen's [24] TRA model to the particular domain of user acceptance of computer technology, replacing the TRA's attitudinal determinants (derived separately for each behavior) with two variables: perceived ease of use and perceived usefulness, employed in computer usage contexts. It argues that behavior (computer usage) is strongly affected by perceived usefulness [1, 20]). Adams et al. [1] also reported that perceived ease of use has direct effects on both perceived usefulness and usage. Additionally, Davis et al. [20] suggest that there are external variables that affect both perceived ease of use and perceived usefulness. Figure 1 presents the model examined in this study. It integrates these existing theories and extends TAM by incorporating self-efficacy explicitly in the model. The importance of self-efficacy

Self-efficacy is associated with beliefs and behavior [6-8, 34, 36]; it also has a critical influence on decisions involving computer usage and adoption [15, 20, 23, 45, 59]. Individuals who consider computers too complex and believe that they will never be able to control these computers will prefer to avoid them and are less likely to use them. Gist [35] also suggests that

Computer Experience

Self-Efflcacy~ , b I

self-efficacy is an important motivational variable, which influences individual affect, effort persistence and motivation. The relationship between self-efficacy and perceived usefulness is meant to present the effect of self-efficacy on motivation as well as on outcome expectations. Additionally, individuals who feel less capable of handling a situation may resist it because of feelings of inadequacy or discomfort which may result from expected changes. On the other hand, individuals with high self-efficacy will perceive the system to be easy and useful due to the effect of self-efficacy on the degree of effort, the persistence and the level of learning which takes place [6] and will be less resistant to changes. Individuals' perceived ability to use a product successfully affects their evaluative and behavioral response to the product [23]. Therefore, self efficacy is likely to affect beliefs and behavior. Specifically, it will affect system usage directly and indirectly through perceived ease of use and perceived usefulness. The detailed hypotheses to be introduced below are based on the idea of structural equation modeling techniques [25]. The specific structural equation modeling technique to be applied in this paper will be introduced in greater detail in the Methods section. The path coefficient corresponding to the arrows in Fig. 1 represents the direct effects between the involved variables. An indirect effect represents those effects through the intervening variables; it is the product of the path coefficients along an indirect route from cause to effect via tracing arrows in the headed direction only. When more than one

:,el

Perceived Easeof Use

\

SystemUsage

Computerl ~ , b , , Anxiety I ~ 2b, c,d, e

Organlzatlonsl~f Support F

Perceived Usefulness

Fig. 1. The determinants and consequences of self-efficacy: a computer usage model. Note: the numbered arrows correspond to the hypotheses described in the body of the paper.

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indirect path exists, the total indirect effect is their sum. The sum of the direct and indirect effect reflects the total effect of the variable on the endogenous variable. The hypotheses below are mainly expressed in terms of direct effects. When a significant direct effect is not assumed to exist, the hypothesis only concern the total effect. The determinants o f self-ej~ieacy

Bandura [6-8] identifies several determinants of self-efficacy, including enactive mastery skills and verbal persuasion. From a review of the IS literature, these two variables are operationalized as prior experience and support, respectively. Computer experience is hypothesized to affect self-efficacy expectations positively with perceived successes in task performance raising mastery expectations and failures lowering them. He asserts that experience is particularly influential because of its direct, personal nature. He also hypothesized that verbal persuasion positively affects self-efficacy, where perceived encouragement and support from others raises efficacy expectations. He also states that self-efficacy expectations induced through verbal persuasion are likely to be weaker than those derived from personal experience. Based on the self-efficacy paradigm, the weaker effect of verbal persuasion on self-efficacy is due to the assumption that verbal persuasion does not provide a direct, experiential base. These two factors may also be directly related to behavior and motivation. Specifically, prior experience is hypothesized to affect behavior and motivation to the extent that the individual is able to assess the skill level exhibited in doing the task. This is consistent with the TAM and TRA models, where these three factors are classified as external factors affecting both perceived ease of use and perceived usefulness. The TRA model, as well as TAM, propose that external factors, such as experience, will affect behavior through their effect on beliefs. Computer experience has been found to be associated with self-efficacy, computer anxiety, perceived ease of use and perceived usefulness [34-38, 51, 58, 98]. Gist et al. [37, 38] discovered that computer experience is likely to improve a person's perceptions and beliefs about using the technology by increasing their beliefs in their ability to master the challenge and to reduce any fears they may have. Further, experience is likely

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to have only an indirect effect on usage through self-efficacy and other mediator variables. For example, Hill et al. [45] found that computer experience per se does not directly affect subsequent behavior regarding use of computer technology, but indirectly through changes in perceived efficacy. Thus, experience is hypothesized to have an indirect effect on computer usage through self-efficacy, perceived ease of use and perceived usefulness. Therefore, Hypothesis la predicts that computer experience will have a positive direct effect on self-efficacy. Hypothesis Ib predicts that computer experience will have a negative direct effect on computer anxiety. Hypothesis lc predicts that computer experience will have a positive direct effect on perceived ease o f use,

Hypothesis ld predicts that computer experience will have a positive direct effect on perceived usefulness. I4ypothesis le predicts that computer experience will not have a significant direct effect on computer usage but the total effect will be positive and significant.

Verbal persuasion, i.e. support and encouragement, was also hypothesized to affect beliefs, attitudes and behavior. Individuals rely, in part, on the opinion of others as well as the support and encouragement they receive in forming judgments about their own abilities. Additionally, since individuals need more resources to help them become more proficient, it is expected that higher organizational support would result in higher judgments of self-efficacy on the part of individuals. The availability of assistance to individuals who need it is likely to increase their ability to perform a task. Moreover, support was believed to be an indication of organizational norms regarding use, and this would positively influence outcome expectations and beliefs in addition to self-efficacy. Davis et al. [20] also emphasized that perceived usefulness and perceived ease of use were affected by management support. Further, in testing a subset of the model proposed by Triandis [94, 95]. Thompson et al. [91] reported that facilitating conditions, including support for users, will have a direct effect on utilization. Organizational support has been associated with greater system usage while lack of organizational support has been a critical

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Igbaria, livari--Effects of Self-efficacy on Computer Usage

barrier to the effective utilization of computers [30, 63]. Trevino and Webster [93] also suggested that management support is positively related to perceived ease of use while Igbaria [51] found that it was negatively related to computer anxiety. In accordance with TAM, the effect of the organizational support on usage is supposed to be indirect. Therefore, Hypothesis 2a predicts that organizational support will have a positive direct effect on self-efficacy. Hypothesis 2b predicts that organizational support will have a negative direct effect on computer anxiety. Hypothesis 2c predicts that organizational support will have a positive direct effect on ease of use. Hypothesis 2d predicts that organizational support will have a positive direct effect on perceived usefulness. Hypothesis 2e predicts that organizational support will not have a significant direct effect on computer usage but the total effect will be positive and significant.

The outcomes of self-ej~cacy Anxiety is a "generalized emotional distress" [75] experienced by an individual. According to Bandura, self-efficacy is negatively affected by emotional arousal or anxiety. Miura [71] states that less anxious users tend to feel more efficacious. However, anxieties are generally divided into two categories which are trait-based (i.e personality tendencies that are stable over time and situations) or state anxiety (a transitory response to a specific situation). While traitbased anxiety may be considered as antecedent to low self-efficacy, state anxiety is probably caused by low self-efficacy. Computer anxiety is a form of state anxiety and is an irrational emotional distress which is experienced by an individual when using or considering the use of computer technology. Bandura [6] and Stumpf et al. [86] found that individuals experience anxiety in attempting to perform behaviors they do not feel competent to perform. Bandura [7, 8] argues that self-efficacy beliefs function as proximal determinants of behavior (here, computer usage) and motivation (perceived usefulness and perceived ease of use). Gist links self-efficacy to expectancy, where expectations influence action. She suggests that the

behavior-outcome relationships, such as effort to performance, should be considered analogous to self-efficacy, because "expected performance outcomes depend heavily on the type of behaviors an individual chooses to execute" [36] (p. 185). Bandura also distinguishes between self-efficacy and 'outcome judgment', which is similar to perceived usefulness. He states that, "In any given instance, behavior would be best predicted by considering both self-efficacy and outcome beliefs" [7] (p. 140). Empirical support for the effect of both self-efficacy and outcome beliefs on computer acceptance was found by Hill et al. [36]. They observed that self-efficacy with respect to computers plays an important role in determining an individual's decision to use them. However, Gist [36] argues that the complexity of the task involved will affect the strength of the relationship between self-efficacy and performance. This suggests that perceived difficulty of performing a task (low perceived ease of use) may mediate the relationship between self-efficacy and performance. Specifically, we hypothesize that self-efficacy will affect computer usage as well as perceived ease of use and perceived usefulness. Therefore, Hypothesis 3a predicts that self-efficacy will have a negative direct effect on computer anxiety. Hypothesis 3b proposes that the individual's self-efficacy will have a positive direct effect on his~her perceived ease of use. Hypothesis 3c proposes that the individual's self-efficacy will have a positive direct effect on his~her perceived usefulness. Hypothesis 3d proposes that the individual's self-efficacy has a positive direct effect on his~her use of computer technology.

A number of studies have documented the importance of computer anxiety as a key variable related to perceived usefulness and usage [33, 52, 73]. Computer anxiety was found to have a negative impact on constructs similar to perceived usefulness [52]. People who are less anxious (computerphrenics) are much more likely to interact with computers than people who are more anxious (computerphobics). Davis et al. [20] also suggested that computer anxiety should be brought into future analysis when examining the factors affecting computer usage. Computer anxiety has been linked with negative beliefs (perceived usefulness) about computers, problems in playing with them, and avoidance of

Omega, Vol. 23, No. 6 the technology [52, 97]. Individuals who feel comfortable with the machine are more likely to produce desired consequences. On the other hand, individuals who experience high levels of anxiety are likely to behave more rigidly than individuals whose level of anxiety is relatively low. Therefore, Hypothesis 4a predicts that computer anxiety will have a negative direct effect on perceived ease of use. Hypothesis 4b predicts that computer anxiety will have a negative direct effect on perceived usefulness. Hypothesis 4c predicts that computer anxiety will not have a significant direct effect on computer usage but the total effect will be negative and significant. Based on TAM, perceived ease of use and perceived usefulness are thought to be potentially important determinants of system usage. Davis et al. [20] found perceived ease of use to have a strong effect on perceived usefulness. Their work was replicated by Adams et al. [1] who reported that both are important in affecting individuals' decision to use the system (mainly in their first study) and that perceived ease of use strongly affects perceived usefulness. Mathieson [69] also found that perceived ease of use explains a significant amount of the variance of perceived usefulness and that perceived usefulness and perceived ease of use contribute to behavior. Additionally, several studies on innovation adoption and diffusion have examined the complexity of innovations [92, 103]. These studies imply high complexity is linked to low perceived ease of use [72] on the adoption of new technologies [82]. Perceived complexity has been generally recognized as a factor inhibiting the diffusion of technology [45, 55], because increased complexity of an innovation requires increased effort on the part of the adopter, thus decreasing the likelihood of adoption [22, 46, 54]. Thompson et al. [91], for example, observed that perceived complexity has a direct effect on PC use. Therefore, Hypothesis 5a predicts that perceived ease of use will have a positive direct effect on perceived usefulness. Hypothesis 5b predicts that perceived ease of use will have a positive direct effect on system usage.

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The finding that the use of computer systems is driven to a large extent by perceived usefulness [1, 20] can be explained by motivation theory which argues that if an individual perceives an activity to be instrumental for achieving valued outcomes, he or she will be more likely to accept the new technology. It has been pointed out that perceived usefulness appears to exhibit a stronger and more consistent relationship with usage behavior than other variables reported in the literature including various attitudes, satisfaction and perception measures [19]. Furthermore, the IS literature more generally suggests that perceived usefulness or equivalent measures are positively associated with system usage [45, 50, 80, 81]. Davis et al. in testing the effect of perceived usefulness on both attitudes and behavior, stated that attitude "was generally not found to intervene between beliefs and intentions" [20] (p. 994). Mathieson [69] found that perceived usefulness is the major determinant of people's intentions to use computers, and Thompson et al. [91] observed that job fit, paralleling the definition of perceived usefulness, had a strong effect on PC use. Therefore, Hypothesis 6predicts that perceived usefulness will be positively related to usage of computer technology. To summarize, we suggested above a number of hypotheses related to the extended technology acceptance model incorporating self-efficacy. In accordance with TAM, perceived usefulness was considered the major determinant of computer usage followed by perceived ease of use. Self-efficacy was proposed to be an antecedent of perceived ease of use and usefulness that had mainly indirect effects on usage through ease of use and perceived usefulness. We also examined the determinants of self-efficacy, i.e. experience and support. The conceptual model illustrating the research hypotheses is shown in Fig. 1. METHOD

Sample and procedure The data for this study were gathered by means of a questionnaire survey administered in Finland during Spring 1993. Initially, the top 120 companies in Finland in terms of net sales [89] were selected for the survey. Because of mergers, bankruptcies, and problems in making contact,

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the participants were drawn from a sample of 86 corporations. Of the 86 companies contacted, 81 agreed to participate in this study. Within each company, contact persons were identified and were informed by telephone of the purpose of this study and were asked to describe the type of individual who would be participating. The contact persons were usually IS managers. Contact persons in the participating companies were asked to distribute 10 surveys to individuals who were not EDP professionals or intermediary users (secretaries) and who used computers at work. Participation in the study was voluntary and people were assured that their individual responses should be treated confidentially. A total of 806 questionnaires were sent to the contact people in the 81 participating companies (two companies wished fewer than 10 questionnaires). It was up to the contact person to decide whom would receive the questionnaires. The exclusion of incomplete and returned questionnaires resulted in a final sample of 450 users from 68 companies, a response rate of 55.8%. The high response rate may be due to the fact that the managers encouraged participation, the survey was translated into the local language, and the issue being examined was of current concern. The number of employees in the 86 companies varied between 89 and 28,859; and the corporation's net sales ranged from 639 million Finnish marks ($127 million) to 57 billion Finnish marks ($11.4 billion) [90]. Small companies were mainly in the merchandising industry. When size was measured in terms of employees, there were 23 manufacturing and only 2 merchandising companies among the top 25 companies in the initial sample of these companies, whereas among the smallest 25 companies there were 17 merchandising and 4 manufacturing companies, and 4 in other industries or could not be classified [90]. The average size of companies was 6469 employees in manufacturing and 1580 in merchandising in the initial sample. The individual respondents were largely employed in two major industries: manufacturing (50%) and merchandising (30%). They held professional/non-managerial (51.9%) and managerial positions in a wide range of functional areas including accounting and finance (30.3%), marketing and sales (14.1%), personnel and general management (13.6%), technical fields, such as operations and production, R&D,

Table 1. Profile of the participants Age: Organizational tenure: Job tenure: Gender:

Average = 38.92 Average = 10.45

SD = 8.32 SD = 8.32

Median = 39.0 Median = 7.5

Average = 5.46 Male = 53.6%

SD = 5.32 Median = 3.0 Female = 46.4%

Education: Less than high school Trade school High School Some college Bachelor's Degree Graduate Degree Other

9.8% 4.9% 4.7% 45.3% 10.3% 23.9% 1.1%

Organizational level: Non-management/professional staff First level supervisor Middle management Top management/executives Unclassified

51.9% 16.7% 25.2% 3.1% 3.1%

Division~Functional area: Accounting and finance Marketing and sales Technical areas General mangement and personnel Others

30.3% 14.1% 17.7 % 13,6% 24.6%

Industry: Manufacturing Merchandising Other

50.0% 30.0% 20.0%

engineering, IS (17.7%), and others (24.6%). The majority of were middle levels, and the rest were first level supervisors and executives. Of the 450 participants, 53.6% were males and 46.4% were females. Age ranged from 21 to 61 years, and the mean age of the respondents was 39 years (SD = 8.32). Approximately 45% had completed some college work, and an additional 35 % of the participants were college graduates. The rest of the respondents had high school diploma or lower. The average length of service in the current organization (organizational tenure) was 10.45 years (SD = 8.31) and the length of tenure in their current job averaged 5.46 years (SD = 5.32). Table 1 summarizes the demographic characteristics of our sample. The success of our sample is reflected in the non-significant differences between users in different industries, organizational level, and functional areas. In view of the high association between size and industry, the non-significance differences of industry suggests that the company size is not a significant confounding factor in the sample. Further analyses indicated that there were no significant differences among users employed in different industries (manufacturing, merchandising, and others) and functional areas (technical vs non-technical) in terms of their

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demographic characteristics (i.e. age, organizational tenure, and job tenure). The only demographic variables on which users differed significantly were gender and education in technical vs non-technical functional areas (F = 17.11, P < 0.001 and F = 5.21, P < 0.05, respectively), a greater number of females than males as well as a smaller number of educated participants were non-technical users.

Measures SCT and TAM were used to explain a specific behavior (usage) toward a specific target (computers) within a specific context (users in Finland). The measures correspond to the the use of computers in their organizations in Finland. Usage of the computer technology. Based on several studies [14,21, 53] two indicators of microcomputer usage were included in this study: (1) perceived daily use of microcomputers; and (2) perceived frequency of use of microcomputers. Individuals were asked to indicate the amount of time spent on the microcomputer per day, using a 6-point scale ranging from (1) 'almost never' to (6) 'more than 3 hours per day'. Frequency of use has been suggested by Raymond [78] and used by Igbaria et al. [53] and provides a slightly different perspective of use than time. Frequency of use was measured on a 6-point scale ranging from (1) 'less than once a month' to (6) 'several times a day'. These indicators are typical of the kinds of self-reported measures often used to operationalize system usage, particularly in cases where objective usage metrics are not available. Objective use logs were not practical in the present study since participants used different microcomputers as well as different applications for different tasks. Self-reported usage should not be regarded as precise measures of actual usage, although previous research suggests they are appropriate as relative measures [10]. Perceived usefulness. This measure is defined as "the prospective user's subjective probability that using a specific application system will increase his or her job performance within an organizational context" [20] (p. 985). The items used to construct the perceived usefulness scale were adapted from prior research [19, 20, 50], with appropriate modifications to make them specifically relevant to microcomputers. Individuals were asked to indicate the extent of

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agreement or disagreement with the following four statements concerning microcomputers on a 5-point Likert-type scale ranging from (1) strongly disagree to (5) strongly agree: "Using microcomputers improves my job performance"; "Using microcomputers increases my productivity in the job"; "Using microcomputers provides me with information that would lead to better decisions"; and "Using microcomputers enhances my effectiveness in the job". Perceived ease of use. Based on [19] and [20] perceived ease of use refers to the degree to which computer technology is perceived as relatively easy to understand and use. Individuals were asked to indicate the extent of agreement or disagreement with the following four statements concerning microcomputers on a 5-point scale ranging from (1) strongly disagree to (5) strongly agree: "Learning to use microcomputers is easy for me"; "2 find it easy to get microcomputers to do what I want to do"; "It would be easy for me to become skillful at using microcomputers": and "I find microcomputers easy to use." Computer anxiety. This refers to the tendency of an individual to be uneasy, apprehensive, and/or phobic towards current or future use of computers in general. The computer anxiety items were selected from the original scale developed by Raub [79] and validated by Igbaria and Chakrabarti [52]. The instrument asks individuals to indicate their agreement or disagreement with 3 statements reflecting anxiety, apprehension, confusion, hesitation etc. in using computers in general (e.g. "I hesitate to use a computer for fear of making mistakes I cannot correct"). The response options, anchored on a 5-point Likert-type scale, range from (1) strongly disagree to (5) strongly agree. Self-efficacy. Self-efficacy was measured using a two-item scale adapted from [45]. Individuals were asked to indicate the extent of their disagreement or agreement with the following two statements on a 5-point scale scale ranging from (1) strongly disagree to (5) strongly agree: "I will understand how computers work"; and "I am confident that I could learn computer applications." Computer experience. Computer experience was assessed by seven items asking respondents to indicate the extent of their experience in using application systems, different types of computer software packages (e.g. spreadsheet, word processing), financial modeling, languages (3rd

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Igbaria, livari--Effects of Self-eJ~cacy on Computer Usage

and 4th generation) and participation in non-technical analysis (feasibility studies and requirements analysis) and technical design of computerized information systems. The overall computer experience was the sum of these experiences. Organizational support. The measure of organizational support, developed by Igbaria [50], incorporated the general support, which includes top management encouragement and allocation of resources. Individuals were asked to indicate the extent of agreement or disagreement with the following four statements concerning organizational support on a 5-point scale ranging from (1) strongly disagree to (5) strongly agree: "Management is really keen to see that we are happy with using our microcomputers"; "Management has provided most of the necessary help and resources to get us used to the microcomputer quickly"; "I am always supported and encouraged by my boss to use the microcomputer in my job"; and "I am convinced that management is sure as to what benefits can be achieved with the use of microcomputers". Data analyses

The hypothesized relationships among the study variables depicted in Fig. 1 were tested by means of Partial Least Squares (PLS). PLS is a second-generation multivariate technique that facilitates testing of the psychometric properties of the scales used to measure a variable, as well as estimating the parameters of a structural model, i.e. the magnitude and direction of the relationships among the model variables [25, 62, 100]. It is a powerful analytical technique in testing structural equation models, and is particularly applicable in research areas where theory is not as well developed as that demanded by LISREL [26]. As suggested by Lohmoller [61] (p. 7) "PLS methods are more close to the data, more explorative, more data analytic". Of particular relevance to this study is the fact that PLS does not depend on having multivariate normally distributed data (distribution-free). Finally, it can be used with non-interval scaled data, and importantly, with small samples. PLS recognizes two components of a causal model: the measurement model and the structural model. Figure 1 represents the structural model being examined. The model describes the relationships or paths among theoretical constructs. Furthermore, for each

construct in Fig. 1, there is a related measurement model, which links the construct in the diagram with a set of items. The measurement model consists of the relationships between the observed variables (items) and the constructs which they measure. The characteristics of this model demonstrate the construct validity of the research instruments, i.e. the extent to which the operationalization of a construct actually measures what it purports to measure. Two important dimensions of construct validity are (a) convergent validity, including reliability, and (b) discriminant validity. The test of the measurement model includes estimation of the reliability coefficients (the composite reliability) of the measures, as well as an examination of the convergent and discriminant validity of the research instruments. In determining the appropriate minimum loadings required for the inclusion of an item within a scale, we used Fornell's [25] recommendation to retain items that loaded highly (0.70 is considered to be a high loading since the item explains almost 50% of the variance in a particular construct) on their respective constructs. Fornell and Larcker's [27] criterion that an average extracted variance should be 0.50 or more was used to assess the average variance extracted for all constructs. We also used the guidelines recommended by Hair et al. [41], in determining the relative importance and significance of the factor loading of each item, i.e. loadings > 0.30 are considered significant; loadings > 0.40 are considered more important; and loadings 0.50 or greater are considered to be very significant. Finally, the criteria suggested by Nunnally [76] were applied to determine the adequacy of the reliability coefficients obtained for each measure. To assess discriminant validity of the measures, i.e. the degree to which items differentiate among constructs or measure distinct concepts, we examined the correlations between the measures of potentially overlapping constructs [40]. If the items comprising a construct correlate more highly with each other than with items measuring other constructs in the model [28, 40], the measure is determined to have adequate discriminant validity. PLS is also used to test the structural model. A structural model is a regression-based technique, with its roots in path analysis, and often loosely termed as a causal modeling technique. It is a relatively new approach to

Omega, Vol. 23, No. 6

testing multivariate models with empirical data [100]. The structural model consists of the unobservable constructs and the theoretical relationships among them (the paths). It evaluates the explanatory power of the model and the significance of paths in the structural model which represent hypotheses to be tested. Together, the structural and measurement models form a network of constructs and measures. The item weights and loadings indicate the strength of measures, while the estimated path coefficients indicate the strength and the sign of the theoretical relationships. The evaluation of the structural model was conducted with the overall sample. The computer program used for this analysis was LVPLS 1.6 (Latent Variables Path Analysis using Partial Least Squares), developed by Lohmoller [61, 62]. To test the estimated path coefficients, t-statistics were calculated using a nonparametric test of significance known as jackknifing [96, 99]. For more information on PLS, the interested reader can refer to [25] and

[62]. The path coefficient of an exogenous variable represents the direct effect of that variable on the endogenous variable. An indirect effect represents those effects interpreted by the intervening variables; it is the product of the path coefficients along an indirect route from cause to effect via tracing arrows in the headed direction only. When more than one indirect path exists, the total indirect effect is their sum. The sum of the direct and indirect effects reflect the total effect of the variable on the endogenous variable [5, 83]. We also examined the study constructs across industries and functional areas (technical vs non-technical). As indicated earlier, there was a significant relationship between gender and education and functional areas among the participants. Therefore, it was necessary to control for both gender and education in these

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Table 2. Assessment of the measurement model

Variables

Computer experience Organizational support Self-efficacy Computer anxiety Perceived ease of use Perceived usefulness System usage

The composite reliability (~ coefficients)

Average variance extracted/explained

1.00 0.86 0.73 0.82 0.94 0.96 0.94

1.00 0.60 0.58 0.61 0,79 0.86 0.88

analyses so that conclusions would not be confounded by gender and education differences across functional areas and industries. Industry and functional area differences in the set of the constructs under investigation were examined using a multivariate analysis of variance (MANOVA) with interaction terms. Industry and functional areas were the independent variables, the study constructs were the dependent variables, and the interactions were gender and education. The results show that both F-tests for industry and functional areas were insignificant ( F = 1.45 and 0.60, respectively). This indicates that no significant differences were found in the study constructs across functional areas and industry. RESULTS The measurement model

Table 2 gives the results of the measurement model. It displays the composite reliability coefficients describing the internal consistency of the measurement model and the average variance extracted. The data show that the composite reliabilities of the constructs range from 0.73 to 0.96, which satisfy Nunnally's [76] guidelines. Furthermore, Table 2 demonstrates satisfactory convergent validity of the constructs. Results, in general, show that the convergent validity of our survey measures was strong, as recommended by Fornell and Larcker [27]. Average variance extracted for all constructs exceeded 0.50. The

Table 3. lntercorrelations among the study constructs Variables 1. Computer experience 2. Organizational support 3. Self-efficacy 4. Computer anxiety 5, Perceived ease of use 6. Perceived usefulness 7. System usage

1 1.00 0.08 0.33 -0.26 0.30 0.22 0.33

2 0.60 0.11 -0.07 0.14 0.20 0.11

3

0.58 -0.14 0.38 0.22 0.15

4

0.61 0.88 -0.15 -0.14

5

6

7

0.79 0.52 0,27

0.86 0.45

0.89

Note: all correlations are significant at 0.05 lower. The diagonals represent the average variance extracted.

Igbaria, livari--Effects of Self-efficacy on Computer Usage

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Table 4. Prediction of self-efficacy and Computer anxiety Self-efficacy

Computer anxiety Direct

Variables Computer experience Organizational support Self-efficacy R2

0.33* 0.07*

-0.24* - 0.02 - 0.7* 0.07*

0.12"

Indirect -0.02 0.00

Total -0.26* 0.02 - 0.07*

*P _< 0.01.

item reliabilities of all the constructs (not presented here) were also significant. The results of the test conducted to assess discriminant validity of the measures revealed no violation of the criteria for discriminant validity. The intercorrelations among the items within the construct were found to be stronger than between items from other constructs (not shown here). Further, Table 3 presents the intercorrelations among the study variables. In all of the 21 entries examined, the squared correlations, representing the shared variance among variables, were found not to exceed the average variance explained. This suggests that our constructs are distinct and unidimensional. Further, confirmatory factor analysis was used to confirm the results of the measurement model. Results of the confirmatory factor analysis using the probability levels for the Z2 test confirmed the existence of the seven constructs/factors. The resulting factor pattern was consistent with the seven factors derived in PLS. In summary, the convergent and discriminant validity of our instruments was satisfactory. Intercorrelations in Table 3 also show that self-efficacy is positively correlated with computer anxiety, perceived ease of use, perceived usefulness and usage (r = - 0 . 1 4 , 0.38, 0.22, P < 0.001, and 0.14, P < 0.01, respectively). Moreover, experience was found to be strongly correlated with self-efficacy (r = 0.33, P < 0.001). Organizational support is significantly correlated with self-efficacy (r = 0.11, P < 0.01). While perceived usefulness, perceived ease of use and experience strongly are correlated with system usage (r = 0.45, 0.27, and 0.33, P < 0 . 0 0 1 , respectively). Further, computer

anxiety is negatively correlated with usage, perceived usefulness and perceived ease of use (r = - 0.14, - 0 . 1 5 , P < 0.01 and - 0 . 3 3 , P < 0.001, respectively). Finally, perceived ease of use is strongly correlated with perceived usefulness and usage ( r = 0 . 5 2 and 0.27, P < 0.001, respectively). Tests o f the structural model

The effects of the external factors on self-efficacy were examined. Data in Table 4 show that, consistent with Hypotheses 1a and 2a, both antecedent variables explained 12% of the variance and had significant direct effects on self-efficacy. The strongest effect is noted for computer experience (7 = 0.33, P < 0.001). A smaller but significant direct effect was observed for organizational support (7 = 0.07, P < 0.01). Consistent with Hypothesis 3a, Table 4 also shows that self-efficacy had a positive direct effect on computer anxiety ((/~ = - 0.07, P < 0.001). Computer experience, as expected (Hypothesis lb), had a strong direct effect on computer anxiety (7 = - 0 . 2 4 , P < 0.001). On the other hand, support had no significant effect on computer anxiety (Hypothesis 2b). Table 5 shows that self-efficacy, consistent with Hypothesis 3b, had a strong positive direct effect on perceived ease of use (/3 = 0.29, P < 0.001). It also shows that computer anxiety, consistent with Hypothesis 3b, had a strong negative direct effect on perceived ease of use (/~ = - 0.26, P < 0.001). Additionally, consistent with Hypothesis 1c, computer experience had a strong direct effect on perceived ease of use (7 = 0.14, P < 0.001). Organizational support,

Table 5. Prediction of perceived ease of use and perceived usefulness Perceived ease of use Variables Computer experience Organizational support Self-efficacy Computer anxiety Perceived ease of use R" *P < 0.01.

Direct 0.14" 0.09* 0,29* -0.26* 0.26*

Indirect 0.16 0.03 0.02

Perceived usefulness

Total

Direct

0.30* 0.12" 0.31" -0.26*

0.06* 0.14" 0.01 -0.04 0.49* 0.30*

Indirect 0.16 0.06 0.16 -0.13

Total 0.22* 0.20* 0.17" -0.17" 0.49*

Omega, 1Iol. 23, No. 6 Table 6. Prediction of system usage Variables

Direct

Computer experience Organizational support Self-efficacy Computer anxiety Perceived ease of use Perceived usefulness R~

0.24* 0.01 0.03 - 0.02 0.01 0.41 * 0.26*

Indirect 0.11 0.09 0.08 - 0.07 0.20

Total 0.35* 0.10" 0.11 * - 0.09* 0.21" 0.41 *

*P < 0.01.

consistent with Hypothesis 2c, had a significant but not a strong direct effect on perceived ease of use (7 = 0.09, P < 0.001). These variables, combined, explained 26% of the variance of ease of use. Note that experience had a strong indirect effect on perceived ease of use, through self-efficacy and computer anxiety. The results reported in Table 5 also show that the model variables explained a significant variation in perceived usefulness (R2= 0.30, P < 0.001). Consistent with Hypothesis 5a, perceived ease of use had a very strong direct effect on perceived usefulness (fl = 0.49, P_< 0.001). Additionally, organizational support, consistent with Hypothesis 2d, had a significant direct effect on perceived usefulness (7 = 0.14, P < 0.001). In accordance to Hypothesis ld computer experience had a significant direct effect on perceived usefulness (~ = 0.06, P _< 0.001). While computer anxiety and selfefficacy, contrary to Hypotheses 3c and 4b, had insignificant direct effects on perceived usefulness, they had strong indirect effects through perceived ease of use. Also, the total effects are

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significant. These results suggest that perceived ease of use plays a very important role in mediating the relationships between experience, anxiety and self-efficacy and perceived usefulness. Consistent with Hypothesis 6, perceived usefulness had a strong direct effect on system usage ( f l = 0.41, P_