This is an earlier version of the paper subsequently published as McQueen, R.J. and Mills, A.M. (1998), End User Computing Sophistication in a Large Health Services Organization, Effective Utilization and Management of Emerging Information Technologies - 1998 Information Resources Management Association Conference, Khosrowpour, M. (Ed), pp. 263-276. [Publisher: Idea Group Publishing, Hershey, PA]
End user computing sophistication in a large health services organization Robert J. McQueen* and Annette M. Mills** *Department of Management Systems University of Waikato Hamilton, New Zealand Phone: 64 7 838 4126 Fax: 64 7 838 4270 Email:
[email protected]
**Department of Accounting, Finance and Information Systems University of Canterbury Christchurch, New Zealand
ABSTRACT This paper reports on a study in a large health services organization which investigated the factors which may affect end user sophistication in the use of computers. Quantitative data were gathered by survey from 194 computer users and structural equation modelling was used to test a conceptual model of user sophistication. The research findings emphasised the importance of computer self-efficacy, on-the-job opportunities to use computers, requisite training, and computer use; all were found to have positive effects on user sophistication. Task uncertainty was shown to negatively impact user sophistication. Contrary to expectation, organizational support had a negative effect on user sophistication. Important corroboratory evidence was also found for the role of computer self-efficacy in explaining IT-related behaviours. Overall, the research findings advance current understanding of how user sophistication is developed in, and influenced by, the working environment in this health organisation. The study concludes with a discussion of the implications for practitioners regarding the assessment and development of computing skills in the workplace. Key words: End-user computing, user sophistication, self-efficacy, health, New Zealand
USER SOPHISTICATION One of the most perplexing issues, which has plagued information systems (IS) practitioners and researchers alike, concerns the payoffs from organisational investments in information technology (IT). Recent studies suggests these expected payoffs are to be found, not in positive impacts on business performance, but in higher productivity and significant value created for consumers (Hitt and Brynjolfsson 1994). Yet, some organisations are lacking in necessary IS knowledge and specific computing skills; indeed, some users are satisfied with learning just enough about the technology to assist their every day tasks (Cragg and Zinatelli 1995; Mirani and King 1994). This lack of expertise and motivation towards skills development can have debilitating effects on factors such as productivity improvements and IS growth and sophistication (Cragg and Zinatelli 1995; Mahmood and Mann 1993). Deriving business gains through IT investment is likely to require, among other factors, sophisticated users individuals who are able to go beyond basic computing towards using IT for operational and strategic gain. Efforts to enhance IT investment value should therefore include an understanding of the factors that further worker sophistication in using computing technology. Except for a few studies regarding instrument development and some aspects of user ability (Huff et al. 1995; Nelson and Cheney 1987; Rahman and Abdul-Gader 1993), the agenda for user sophistication research has received little attention. In considering the gaps in the literature, there is a need to investigate how and why sophistication is developed. This research therefore seeks to extend current understanding of user sophistication by investigating the factors which determine the development of user sophistication.
In their endeavours to study the different user types, some researchers have developed typologies for 'pigeon-holing' individual users and for assessing the varying levels of computing competency exhibited (Rockart and Flannery 1983), while others have used applications development as a benchmark to distinguish the more sophisticated user from the less sophisticated user (Mirani and King 1994). However, users have evolved, over time, into subtle variations of these characterisations and can no longer be neatly slotted into the finite sets of categories developed. Researchers are now examining user ability in terms of knowledge and skills across a number of computing knowledge areas (Nelson and Cheney 1987; Rahman and Abdul-Gader 1993). One such approach, labelled the 'sophistication' view of end-user computing capability, encapsulates the general measures of ability, such as knowledge and understanding of basic computing concepts, and familiarity with specific applications, but goes further by endeavouring to capture more detailed information on different types of abilities (Marcolin et al. 1993). This view of user sophistication is conceptualised as three independent dimensions of user capability: breadth of capability, depth of capability, and finesse. Breadth of capability refers to the extent or variety of different tools, skills, and knowledge that an individual possesses and can apply to his or her job. This assumes at least minimal competence in the relevant computing domains. Depth of capability is assessed by the completeness of the user's current knowledge of a particular computing domain. This includes mastery of the features and functions of different types of application systems, practices, and techniques, and the degree to which these tools can be applied through the range of tasks for which they were designed. Finesse describes the ability to creatively apply computing technology. Embracing more than a comprehensive grasp of the commands and capabilities of certain application packages and technologies, finesse includes innovativeness and creativity in the practical use of the technology and an ability to find new or unusual ways of effectively using the technology in the organisation. Three areas of computing knowledge (the major computing domains) are identified as important to an evaluation of user capabilities. These are: hardware, software, and computing concepts and practices. To further user sophistication, it is important that users are able to obtain experiences that push back the boundaries of current computing knowledge and skills. This discussion advances a number of factors as predictors of user sophistication; these are expected to impact user sophistication through the acquisition of requisite experiences for skills development. Training is identified in the literature as a key factor influencing user ability and the acquisition of computer skills (Compeau and Higgins 1995a; Nelson and Cheney 1987). Education was found to be related to breadth of capability (Huff et al. 1995). Organisational support which addresses user needs and supports users in their use of new software applications can also be expected to augment the development of user sophistication. For example, Mirani and King (1994) found that the support needs of users increased uniformly and fairly steeply along the user sophistication spectrum from non-programming users through to functional support users. Task uncertainty and computing opportunities are also considered in this study, as important for skills development. For example, Wood (1986) observed that variances in task uncertainty produced changes in the knowledge and skill requirements for successful task performance; hence, task performance was lowered in the presence of high levels of task uncertainty. In this study computing opportunities was conceptualised as opportunities to develop sophistication (i.e., the extent to which users are provided with or actively obtain computer-related experiences that enhance current skills) and task-technology fit. Prior research suggests an association between opportunities to use technology and skill development (Kasper and Cerveny 1985; Rivard and Huff 1985). Task-technology fit also has important implications for user sophistication by providing users with on-the-job opportunities to use acquired computing skills (Schiffman, Meile and Igbaria 1992; Wood and Bandura 1989). Research also shows computer use to be an important consideration for user sophistication (Huff et al. 1995; Nelson and Cheney 1987). Finally, Bandura's (1977) perspective on self-efficacy has been particularly useful in the study of IT-related behaviours (Compeau and Higgins 1995b; Igbaria and Iivari 1995). Self-efficacy in computing behaviour is defined as computer self-efficacy, that is, "a judgement of one's capability to use a computer" (Compeau and Higgins 1995b, p. 192). Computer self-efficacy is concerned, not with past achievements, but with judgements of what can be accomplished in the future; it does not refer to simple component skills (like formatting diskettes), but incorporates judgements of one's ability to apply those skills to broader tasks (e.g., preparing written reports). Given the appropriate skills and adequate incentives, self-efficacy can be expected to influence factors such as choice of activities and environment, effort and
perseverance in the face of difficulties, and whether thought patterns are self-hindering or self-aiding (Bandura 1977). Computer self-efficacy has important implications for user sophistication; for example, the role of self-efficacy is welldocumented as a key variable in respect of intentions to learn about computers, skill development and higher performance levels, and user sophistication (Compeau and Higgins 1995a; Hill, Smith and Mann 1987; Huff et al. 1995).
THE HEALTH ORGANIZATION STUDIED The health organization, which we will subsequently refer to as HEALTH, is located in New Zealand, and employs approximately 5,000 employees at one primary and seven secondary hospital locations. The research investigation focused on administrative and support staff of the main hospital, although complete separation of administrative and medical staff would be impossible, due to task overlap in many positions. The computer-based work functions of medical and nursing staff seemed to be primarily menu-driven access to the medical information systems for performing tasks such as patient admissions and the referencing of patient records, and as such were unlikely to fall within the areas we wished to investigate. Through access to user lists, we were able to identify a population of 604 computer users that were primarily administrative or support workers. Other survey administrators in the organization estimated that a useable response rate of 25 percent was likely. It was therefore decided that a population census1, rather than a sample, was more appropriate.
SURVEY INSTRUMENTATION In this study, survey instrumentation first considered a number of existing measurement instruments. Based on the research model, a supporting research instrument was developed and evaluated using an iterative series of reviews and modifications. These included academic and peer reviews, and participant evaluation. The feedback of each of these reviews was then used to direct the modifications to the research instrument. Where a well-developed instrument existed, which was appropriate to the level of analysis and detail required, this was also included in the review process.
Operationalising the Study Variables The following measures correspond to the constructs examined in this research: User sophistication was assessed as breadth of capability, depth of capability, and finesse (Huff et al. 1995). For breadth of capability and depth of capability, respondents were asked to indicate (for each of 31 items) whether or not they had used, or had any knowledge of the particular item and if so, to rate their current level of knowledge on a 7point scale ranging from (1) "Very limited knowledge" to (7) "Complete knowledge". An index for breadth of capability was calculated as the number of items which the respondent had used or knew about. The index for depth of capability was assessed as the sum of the scale responses. To assess finesse, the respondents recorded, for each of five items, the response (on a corresponding 7-point scale) which best approximated their ability to creatively use computer tools and technology. The sum of the scale responses was used as an index for finesse. Computer self-efficacy was measured in terms of efficacy perceptions and performance over a range of conditions of increasing task difficulty (Compeau and Higgins 1995b). The respondents were asked to indicate whether or not they believed they could accomplish a hypothetical task using an unfamiliar software package, under each of ten conditions, and if so, to rate their level of confidence on a 10-point scale ranging from (1) "Not at all confident" to (10) "Totally confident" in that judgement. Where respondents indicated they could not complete the hypothetical task under a particular condition, a value of '0' was assigned on the confidence scale. Computer self-efficacy was then assessed in terms of magnitude, calculated as the number of "YES" responses given, and strength, taken as the sum of the confidence ratings across the performance levels. Computer use was assessed in terms of frequency of use and actual daily use (Huff et al. 1995). For frequency of use, the respondents recorded on a scale ranging from (1) "Several Times a Day" to (6) "Less Than Once a Month", the 1
A census was required (i.e. 25% of 604 = 151) in order to achieve a target of 150 responses.
response that best approximated how frequently they used a computer. For actual daily use, respondents indicated (for the average working day on which a computer was used) the amount of time spent using the system. Training. For this item, respondents indicated on a 5-point scale ranging from (1) "No training" to (5) "Very extensive training", the extent to which computer training had been received through each of four sources: college courses, vendor training, in-house training, and self-study (Igbaria and Chakrabarti 1990). The training index was determined as the sum of the scale responses. Education. For this item, an existing scale (Igbaria and Chakrabarti 1990) was modified to include the range of educational levels relevant to the research context. Respondents then identified, on a scale ranging from (1) "Some High School or Less" to (8) "Completed Graduate or Professional Degree", the highest level of education attained. Organisational support was measured using an existing 8-item scale incorporating management encouragement and support provided (Igbaria and Chakrabarti 1990). The respondents were asked to indicate on a 5-point scale ranging from (1) "Strongly agree" to (5) "Strongly disagree", the extent of their agreement or disagreement with each statement. An index for organisational support was then computed as the sum of the scale responses. Task uncertainty. Using an existing 4-item scale (House and Dessler 1974), respondents indicated (on the respective 5-point scales), the extent to which tasks were repetitive, structured, similar or varied. The index for task uncertainty was taken as the sum of the scale responses. Computing opportunities were assessed in terms of opportunities to develop sophistication and task-technology fit. For opportunities to develop sophistication, respondents indicated for each of three statements, the extent to which the job afforded opportunities to enhance breadth of capability, depth of capability, and finesse. For task-technology fit, respondents indicated for each of four statements, the extent to which computing technology aided task performance. For both measures, the responses were recorded on 5-point scales ranging from (1) "Not at all" to (5) "To a very great extent", and the respective indices computed as the sum of the scale responses.
SURVEY RESPONSES Survey data were collected by means of a self-administered questionnaire, sent out in the internal mail of the organization to 579 targeted users, which excluded the pre-test participants. Of these, 225 surveys were returned, of which 194 were useable. The survey response rate is shown in Table 1.
TABLE 1: Survey Response Population Size
604
Pre-test Participants
25
Initial Sample Size
579
Non-contacts2 Achievable Sample
42 Size3
537
Number of Responses Received
225
Number of Non-responses
312
Incomplete Responses Number of Useable Responses
31 194
2
Non-contacts are individuals who were included in the initial sample but did not receive a questionnaire due to factors such as attrition, on leave, or addressee unknown.
3
Fife-Shaw (1995) suggests that it is reasonable to adjust the sample base against those respondents who were never in a position to decline participation (i.e., non-contacts). Hence, Achievable Sample Size = (Initial Sample Size Number of Non-contacts).
Non-response Bias Based on the achievable sample size (Fife-Schaw, 1995), the data in Table 1 shows a response rate of 41.9 percent. Lower response rates increase the likelihood of non-response (and response) bias. Three approaches to protect against non-response bias are identified: i) to analyse a sample of non-respondents, ii) to estimate the effects of non-response, and iii) to minimise non-response (Armstrong & Overton, 1977; Kanuk & Berenson, 1975). Sampling Non-respondents Sampling non-respondents was not feasible in the context of this research. Since these persons had been contacted on three occasions concerning participation (i.e., by preliminary notification, the cover letter and survey questionnaire, and reminder notices), it was unlikely that further contact with non-respondents would result in the completion of the 14-page questionnaire. Estimating the Effect of Non-Response Three methods for estimating the effect of non-response are discussed in this section: subjective estimates of nonresponse bias; extrapolation which assumes that the non-respondents are similar to those who respond less readily to the survey; and comparisons with 'known' values for the population or sampling frame (Armstrong & Overton, 1977; Kanuk & Berenson, 1975). Since it was not possible to access the information required to derive a 'subjective estimate' of non-response bias, the extrapolation and 'known' value approaches were considered. A common type of extrapolation is carried out over successive waves4 of a questionnaire and compares the early responses with the late responses. In this survey only seven responses for HEALTH were received in the wave prompted by reminders. The successive waves approach to non-response estimation was therefore not feasible for this research. The time trend basis for extrapolation, in which earlier responses are compared with later responses (within the wave), was also considered. In this study, 75.1 percent of the surveys for HEALTH were returned within the first three days of dispatch. Since it was not possible to determine the time from the respondent's awareness (receipt) of the questionnaire until completion (Armstrong & Overton, 1977) and the number of 'later' responses were few, this approach to estimation was also not feasible. In this study, the only 'known' values were the frequency distributions across departments for the sampling frames. Although an assessment of this statistic is not posited by the researcher as a satisfactory basis on which to estimate the effect of non-response, in the light of limited information, this statistic was considered. A comparison of the distributions across departments for non-respondents and respondents showed that the differences between the distributions were not statistically significant. This suggests that, at least as far as departmental representativeness was concerned, the risk of non-response bias was minimal. Minimising the Rate of Non-response This research endeavoured to pre-empt the potential problems of non-response by minimising the rate of non-response. The implementation of this approach was based on a number of recommended techniques (Cragg, 1991; Dillman, 1978; Kanuk & Berenson, 1975; Lehmann, 1979; Sproull, 1995), which included preliminary notification, cover letters, survey sponsorship (i.e., who was conducting the study and why), use of a well-designed clearly laid out questionnaire, assurance of anonymity and confidentiality, deadline (return) dates, and reminder notices, the use of self-addressed return envelopes and the offer of a summary report to participants
SURVEY ADMINISTRATION The collection and analysis of quantitative data formed the primary investigative thrust of the present research. The collection of quantitative data was carried out by using a self-administered, multi-part, structured questionnaire as the primary instrument of data collection. The survey was administered through the internal mail distribution system of the health organization.
4
Waves refer to the responses generated by a stimulus (e.g., reminder notices).
Table 2 Participants' Demographics Division Division/School Academic Services Community & External Relations Facilities Management Financial Services Information & Tech Services Personnel & Management Schools of Study Student Services Other
f 16 8 6 18 18 5 54 7 2
Organisation Level Organisation Level Strategic Management Middle Management First Level Supervisor Professional Support Staff Technical Support Staff Secretarial/Clerical Staff Other
% 11.9 6.0 4.5 13.4 13.4 3.7 41.0 5.2 1.4 Job Category
f % 4 3.0 12 9.0 21 15.8 26 19.5 11 8.3 54 40.6 5 3.8
Job Category Accounting/Finance Staff Administrators Information Systems Staff Personnel Staff Secretarial/Clerical Staff Other
f 14 37 25 3 44 11
% 10.4 27.6 18.7 2.2 32.8 8.2
Other Demographics Age Organization Tenure Job Tenure Span of Control (Direct) Span of Control (Indirect) Gender
Mean SD 38.8 10.6 7.4 6.7 3.6 4.3 3.3 8.7 23.2 70.4 Female = 132 (68%)
Median Range 39 18-61 5 .25-37 2 .02-23 0 0-70 0 0-450 Male = 61 (31.4%)
Level of Education Education Level Some High School or Less Completed High School Some College/Polytechnic Study Completed College/Polytechnic Study Some University Study Completed Bachelor’s Degree Some Graduate/Professional Study Completed Graduate/Professional Study
f 28 14 24 39 35 21 14 19
% 14.4 7.2 12.4 20.1 18.0 10.8 7.2 9.8
Dillman's (1978) Total Design Method (TDM) approach to survey administration was used to guide the survey design process, data collection, and survey administration strategy. This technique is based on personalising the correspondence throughout data collection in order to maximise the survey response rate. The techniques for survey administration in each study site are described below.
In consideration of the New Zealand Privacy Act (1994) governing individual privacy, all identifying information (e.g., recipient's name, job title, location) were withheld by the organization. A 'blind' approach to survey administration was therefore negotiated with the organization. All correspondence (i.e., letters of introduction and follow-up correspondence) and survey packs were prepared by the researcher and the distribution process facilitated by the organization. Potential participants were initially contacted using a form-letter styled notification of the impending survey and its intent. This initial contact was then followed with the delivery of survey packs.
SAMPLE CHARACTERISTICS In this section the characteristics of the research sample are described using a number of descriptive statistics. These include demographic characteristics and computer use statistics.
Demographic Characteristics The demographic characteristics of the sample extracted from HEALTH are shown in Table 2.
Computer Use Characteristics Computer use was measured in terms of frequency of use and actual use in hours. The frequency distribution, shown in Table 3, indicates the extent to which computer use was a significant part of work activities for the respondents.
Table 3: Computer Use Statistics Frequency of Use Several times a day About once a day A few times per week A few times per month Once a month Less than once a month Number of hours spent on an 'average' day using a computer:
f 130 3 1 Mean: SD: Median: Range:
% 97.0 2.2 0.7 4.1hrs 2.2hrs 4hrs 1/2hr - 12hrs
DATA ANALYSIS AND RESULTS Structural equation modelling defines an approach that simultaneously assesses the measurement model (for construct validity) and the corresponding structural model. In this study, this approach was implemented using Partial Least Squares (PLS), a component-based second generation multivariate technique for data analysis. This technique has been widely accepted among IS researchers as a powerful approach to examining causal models with multiple constructs and multiple measures and as appropriate to the early stages of theory development (Compeau and Higgins 1995b; Igbaria and Iivari 1995; Rivard and Huff 1988). Due to space available, we provide here only a summary of the outcomes of the analysis. Detailed results are available in Mills (1996). Figure 1 presents a graphical illustration of the direct effects between the variables in the research model for the HEALTH sample. Computer-related training, computing opportunities and computer selfefficacy were found to have the strongest effects on user sophistication. Computer use and task uncertainty also had strong direct effects on user sophistication, followed by education which exhibited a small but significant direct
effect. The indirect effect of organizational support on user sophistication through computer self-efficacy, was shown to exceed the value of its direct effect. This had a substantial impact on user sophistication by increasing the overall weighting of the total effect of organizational support. The total indirect effect of computing opportunities on user sophistication was also strong; this significantly increased the value of the total effect of computing opportunities on user sophistication. Computing opportunities were also shown to have the strongest direct effects on both computer self-efficacy and computer use. The direct effects of computer-related training and education on computer self-efficacy were also strong. Computer-related training and task uncertainty were also shown to have strong direct effects on computer use. Contrary to expectations, education had a strong inverse effect on computer use; similarly organizational support also had an inverse effect on computer self-efficacy, computer use, and user sophistication.
Training 0.19 0.11
Education
Computer Self-efficacy
-0.06 0.45 -0.19
0.09 0.36 -0.13
0.23
User Sophistication
0.28 0.14 -0.05
Task Uncertainty -0.26 0.15 0.12 0.23
Computing Opportunities
0.32 0.00
Computer Use
Organisational Support
Figure 1: The Final Research Model - HEALTH
INTERPRETATION AND DISCUSSION OF FINDINGS The primary objective of this research was to examine those factors which determine the development of user sophistication. The results of the tests of the structural model (i.e., the survey findings) provided considerable support for the relationships predicted in the research model. These results identified the direction and strength of impact (both direct and indirect effects) of the determinants on user sophistication, as well as the amount of
variance accounted for by the predictors. In this section, the discussion of the survey findings focuses on these results from the test of the structural model.
THE INDIVIDUAL EFFECTS OF THE ANTECEDENTS In this section, the individual effects of the antecedents are discussed. Each antecedent is introduced, followed by a statement of the research hypothesis, and an interpretation of the corresponding research findings.
Computer Experience In this study, an assessment of the measurement model for computer experience suggested construct overlap. Although the operationalisation of the construct measures endeavoured to capture this a priori distinction between the two variables (i.e., computer experience and user sophistication), the problem of conceptual overlap became evident during model assessment. Since a portion of the observed association between computer experience and user sophistication must then be attributed to instrumentation (rather than to any actual association between the phenomena), therefore exaggerating the association between variables (Zmud et al., 1994), the 'computer experience' variable was excluded from model testing. Previous research has reported a satisfactory level of construct validity for the measurement models for computer experience and user sophistication (Igbaria & Chakrabarti, 1990; Munro et al., 1994;). However, these investigations did not include a simultaneous assessment of the two models; hence the possible problem of multicollinearity would not have been identified. Although Igbaria et al. (1989) did include in the same study, measurement models for user sophistication and for computer experience, their study differed from this research in that, i) sophistication was assessed as one dimension (breadth) and used as one of five indicators of computer use, ii) the investigation focused on a descriptive analyses of the research findings (e.g., frequency distributions and Pearson correlations), and iii) it did not investigate the distinctiveness (i.e., discriminant validity) of computer experience and user sophistication.
Computer-related Training Consistent with theoretical expectation, training was shown to have a positive impact on computer self-efficacy, computer use, and user sophistication. The survey findings showed that individuals who had received more training tended to develop higher levels of user sophistication than those who had received less training. These outcomes corroborate previous research concerning training and the development of user ability (Compeau & Higgins, 1995a; Gist et al., 1988; Igbaria et al., 1989; Kasper & Cerveny, 1985; Rahman & Abdul-Gader, 1993; Yaverbaum & Nosek, 1992). For example, Igbaria et al. (1989) observed that users who had undertaken more training courses tended to use applications which were more varied and sophisticated while Cragg and Zinatelli (1995) observed that lack of training resulted in low computer literacy. This research also confirmed previous findings which suggested that training needs tend to vary along the lines of sophistication (Kasper & Cerveny, 1985; Rockart & Flannery, 1983).Training was also found to impact the development of user sophistication through its effect on computer self-efficacy. This suggests the importance of training in influencing user perceptions (Compeau & Higgins, 1995b; Gist et al., 1989); users who perceive that they are able to develop computing skills (through training) become more confident in their ability to use these skills and will therefore tend to improve their sophistication in computer use. All in all, the research findings suggest that organizations which desire improving on the benefit of their IT investments (through the allocation of training resources for optimal benefit) must train and equip their users with the knowledge and skills required to assist the effective use of computing technology in the working environment (Mahmood & Mann, 1992; Strassmann, 1996). The research findings showed that training significantly impacts computer use, that is, highly-trained individuals are likely to make greater use of the technology than those who have received less training. These results suggest
the training that becomes beneficial to computer use, is that which provides the user with the 'know-how' to make greater use of the technology. These results are also consistent with previous research (Igbaria et al., 1995; Igbaria et al., 1989; Nelson & Cheney, 1987), and underscore the importance of training in influencing computer use. Consistent with Social Cognitive Theory, the results showed that increased training enabled users to develop the self-belief that they can effectively use computing technology in the working environment. Computer-related training may therefore foster increased computer self-efficacy by assisting users to acquire the experience and develop the skills that help strengthen confidence in one's ability to use computers. These results corroborate previous research (Compeau & Higgins, 1995b; Gist et al., 1988, 1989; Igbaria, 1993), and affirm the importance of training for the enhancement of confidence in one's ability to use computing technology.
Education Education was assessed in terms of the level of education achieved by the respondent (no distinction was made in respect of computer-related education). The survey findings indicated that the respondent's education level had a systematic effect on computer self-efficacy and user sophistication. However, the hypothesised effect of education on computer use demonstrated some 'contradiction'. These findings are discussed below. Consistent with theoretical expectation, education was shown to have a small, positive effect on user sophistication. Although these findings did not provide overwhelming support for the research hypothesis, nevertheless, they did provide some confirmation of the work of Munro et al. (1994), who identified a positive association between education and breadth of knowledge. The findings in this research suggested that individuals who had attained a higher level of formal education were likely to be more sophisticated in their use of computers than less-educated users. These findings are consistent with the suggestion that users with a less formal education may be performing computer-related tasks which are simpler, or which expose them to a narrower range of computing tools, than the tasks of more educated users. However, this interpretation assumes that there is a relationship between education and task-related factors. Since this interpretation is beyond the investigative scope of this research, future work may wish to consider this suggestion by examining the relationship between formal education and the characteristics of assigned tasks. An examination of the effect of education on user sophistication (i.e., breadth, depth, and finesse) may provide further information to qualify the current interpretation of effect. Conversely with expectation, education was shown to have a strong, negative effect on computer use; hence individuals who had attained a higher level of formal education were shown to use computers to a lesser extent than individuals with less formal education. Although the reasons for this difference in outcomes are not absolutely clear, it is possible that the direction of effect may be explained by reference to other variables which can be associated with the level of formal education attained. For example, highly educated individuals may be performing tasks which do not require extensive computer use, whereas less educated staff may be performing information-based functions that require extensive computer use. Extensions to this research should therefore consider those factors (e.g., task characteristics) which may provide some evidence to extend the present understanding of the apparent 'contradiction'. In spite of the fact that this research does not provide adequate information from which to infer the correct explanation for the research findings, it does underscore the importance of recognising the limitations to generalising research findings from one context to another. Any endeavour to generalise research findings should pay careful attention to those factors which may inhibit portability. Education was shown to have a positive effect on the formation of computer self-efficacy; this suggests that increasing education boosts the user's confidence towards computing technology. These findings provide some confirmation of previous research (Igbaria, 1993), which suggested that education may impact computer selfefficacy through its negative effect on computer anxiety.
Task Uncertainty In general, the research findings provided evidence to support the hypothesised effects of task uncertainty on computer use and user sophistication. However, contrary to expectation, task uncertainty was shown to have very little impact (at best) on computer self-efficacy. These findings are discussed below. Consistent with expectation, the research findings suggest high task uncertainty inhibits the development of user sophistication, while less task uncertainty assists the development of sophistication. While task variety may encourage users to develop a range of skills (Igbaria, 1990, 1992; O'Reilly, 1982) to meet the requirements of a varied task portfolio, their development of domain-specific knowledge (depth of capability) may be inhibited; these users are likely to develop the ability to use parts of the computer system but exhibit a relatively low level of domain-specific expertise. Consistent with expectation, task uncertainty was shown to have a strong, positive impact on computer use. These findings are consistent with the idea that the length of time for which an individual uses a computer may be consequential of task design factors (e.g., task structure) and the number of tasks which require or would benefit from computer use. For example, Guimaraes et al. (1992) found that decision support system use increased for tasks which were more difficult, more uncertain, and more varied. Mykytyn and Green (1990) observed that as task complexity increased, users took a longer time to complete the task. Igbaria (1990) found that task structuredness was positively associated with computer use. Task variety and task structuredness may have different effects on computer use. For example, high computer usage may be the result of fewer, but more complicated tasks, which require the user to use the technology for longer periods. Computer usage time may also be a function of a varied task portfolio in which the tasks are simple, but collectively, require a great deal of computer time. The interpretative accuracy of the research findings is limited since this work does not provide adequate information to assess how much of the observed effect is due to task variety (i.e., having a number of tasks which require the use of a computer), and how much may be due to the influence of task structuredness. Further research may therefore consider an assessment of the individual effects of task variety and of task structuredness on computer use. This may extend the present understanding of how task uncertainty influences computer use. Nonetheless, the findings of this research do underscore the importance of the task environment to computer use. A small negative effect was identified between task uncertainty and computer self-efficacy. This finding provides only weak support (at best) for a relationship between task uncertainty and computer self-efficacy. It also provides only weak confirmation of previous research which suggested that task uncertainty may be expected to influence the user's assessment of the amount of effort required for successful task performance (Bandura, 1977; Puffer & Brakefield, 1989; Wood, 1986). Overall, this finding implies (in the context of this research) that task uncertainty may not have a meaningful impact on the formation of computer self-efficacy. A reason for this outcome may lie in the assessment of self-efficacy in this research as a task-specific phenomenon (i.e., computer self-efficacy), and of task uncertainty in a more generic (non-domain specific) form. In this study, the assessment of the effect of task uncertainty on computer-self-efficacy assumes that the formation of computer self-efficacy is, to some extent, stimulated by information sources which are not all 'domain-specific' (e.g., the formation of computer self-efficacy may be influenced by self-efficacy generality). Since, overall task uncertainty did not exhibit a strong, direct effect on computer self-efficacy, this suggests that the relationship between task uncertainty and computer self-efficacy may be influenced by the effect of task uncertainty on intervening variables or belief systems, which in turn, affect computer self-efficacy. For example, research has shown that initial selfefficacy can influence the effect of an intervention on post-intervention self-efficacy (Gist et al., 1991; Saks, 1994). Future research may therefore wish to consider the effect of other belief systems and intervening variables that may influence the outcomes for task uncertainty, thereby extending current knowledge regarding the relationship between task uncertainty and the formation of computer self-efficacy.
Computing Opportunities Two indicators were used to define computing opportunities: i) task-technology fit, and ii) opportunities to use computers. For each of these indicators, a measurement scale was developed; the test of the measurement model showed that the indicators were consistent with the latent variable which they were intended to represent. Consistent with expectations, computing opportunities were shown to have the strongest effect (of all the determinants) on user sophistication, computer use, and computer self-efficacy. This emphasises the overall importance of computing opportunities for each of these variables particularly, the development of user sophistication. These relationships are now discussed in detail. Consistent with theoretical expectation, opportunities to use computers were shown to have a strong positive impact on user sophistication. The magnitude of the total impact of computing opportunities on user sophistication was also shown to be strongly and positively influenced by the effect of computing opportunities through computer self-efficacy. These findings provide evidence corroborating the importance of computing opportunities to the development of user sophistication; individuals who have more opportunity to develop sophistication and to use computers to assist task performance (i.e., task-technology fit), tend to become more sophisticated in computer usage than those who have fewer opportunities. This investigation of computing opportunities corroborates the findings of previous research. For example, Rivard and Huff (1985) observed that users who spend a considerable amount of time using software tools soon become quite sophisticated in their use of computers, while a lack of opportunity to practice techniques which go beyond base knowledge and skills, impedes skill development. Schiffman et al. (1992) also found that the user's task environment provided opportunities for end-user programmers to apply their computing knowledge. In summary, the research findings provide strong support for the primacy of computing opportunities in supporting the development of user sophistication. If such opportunities are not available, users are unlikely (outside personal interest) to be proactive in developing their level of sophistication. Consistent with expectation, this research showed that individuals who have more opportunity will tend to spend more time using a computer. Indeed computing opportunities were shown to have the strongest impact (of all the other determinants) on computer use; their total impact was further strengthened through their influence on computer self-efficacy. These results affirm the importance of this variable in respect of computer use, and corroborate the findings of previous research which suggested that computer use is a function of task characteristics such as task-technology fit and job requirements (Goodhue & Thompson, 1995; Thompson et al., 1991). These results further suggest that for many, computer use may be a function of task design considerations (Goodhue & Thompson, 1995) that 'require' or facilitate computer use. These findings therefore underscore the dominance of task considerations in respect of computer use. The research findings provided strong support for the relationship between computing opportunities and computer self-efficacy; these suggested that individuals who have more opportunities to use the computer to assist their work tend to develop a higher sense of confidence in their computing ability. For example, consistent with Social Cognitive Theory, computing opportunities provide users with occasions for building confidence through successful task performance; this has been shown to exert a stronger, more lasting effect on efficacy perceptions than other sources such as verbal persuasion (Bandura, 1986). These findings are also consistent with the idea that computing opportunities positively impact the formation of computer self-efficacy, providing necessary resources and minimising the constraints that inhibit successful performance (Gist & Mitchell, 1987).
Organizational Support Contrary to expectation, negative effects were observed for organizational support in respect of computer selfefficacy, computer use, and user sophistication. Although the reasons for these findings are not entirely clear, an understanding of the nature of organizational support in the research context, will assist in the interpretation of the findings.
In this research, organizational support was shown to function primarily as a provider of technical support (e.g., hardware and software installation), and as a problem-solving mechanism to enable users to get on with their use of computers while minimising interruptions. Users therefore tend to refer to support only when assistance was required. Organizational support also facilitated the development of basic computing skills among users and the introduction and use of new computing technologies, through training. Contrary to expectation, organizational support was shown to have a small negative impact on the development of user sophistication, which suggests that less sophisticated users rely more heavily on organizational support, than more sophisticated users. These results are contrary to previous research which suggested that the support needs of users tend to increase along the lines of sophistication (Mirani & King, 1994; Rockart & Flannery, 1983). Support has also been shown to play an important role in introducing users to new tools and in responding to the user's needs and problems (Igbaria, 1990; Rainer & Carr, 1992). From a theoretical perspective, it seemed reasonable to suggest that increasing levels of organizational support would lead to increasing levels of user sophistication, since the user would have greater access to the resources required to increase proficiency. However, the research results suggested not only that organizational support had little impact on the development of user sophistication but that this impact was also negative. This suggests that although there may be a need among more sophisticated users for organizational support, the nature of the support provided does not necessarily meet these needs. Hence, it may be suggested that the need for organizational support may not equate with use of organizational support. Indeed, Rockart and Flannery (1983) observed that although end-user support personnel were reasonably fluent in a range of end-user tools, some enduser programmers were more sophisticated in the knowledge of one or two software products which were particularly relevant to their functions. This would suggest that the more sophisticated user is likely to be more self-sufficient and to make fewer demands on organizational support. This explanation is consistent with research findings which found that reliance on support mechanisms decreases as familiarity increases (Rainer & Carr, 1992). Another explanation for the 'contrary' research findings concerns the nature of the support provided. While training may be readily available, such training tended to be introductory, equipping users with a basic competency across a few computing domains. Although this level of training may support expansion of the users' breadth of knowledge, these users were likely to remain novices since they did not possess an in-depth understanding of one, or a few domains. This suggests that although the 'amount' of support provided may increase, this may have little impact on user sophistication beyond an extension of basic competencies across a few domains. These research findings are also consistent with the idea that a well-developed system of IS support and peer support hinders the development of user sophistication. In such environments, the development of personal skills may become neither necessary, nor urgent because there is someone on-hand to solve problems and to assist when difficulties are encountered. Users may then tend to rely on external assistance and never try to develop a level of computing competency that goes beyond basic requirements. Although the statistical estimate of the total effect of organizational support on user sophistication did not suggest a strong relationship between the two variables, the research findings did show the total indirect effect of organizational support on user sophistication to be stronger than its direct effect. This suggests that the effect of organizational support on user sophistication is mediated, in part, by its effect on intervening factors. For example, prior research suggests that IS support has strong implications for computer use. This points to the importance of the peer network system in encouraging and supporting the user through verbal persuasion and 'healthy competition'. In general, the findings corroborate the importance (and need for) a support system (including, training, management encouragement, and application support) which encourages skills development by providing the tools and the 'know-how' to increase computing knowledge. The 'contradictory' findings observed in this research suggest that although increased organizational support may not lead to an increase in user sophistication, the presence and nature of the support an the organization, is fundamental to appropriate and effective use of computing technology.
Contrary to expectation, the research results showed that organizational support had no effect on computer use. These findings are inconsistent with previous research (Igbaria et al., 1995; Igbaria & Iivari, 1995; Schiffman et al., 1992; Thompson et al., 1994). A possible explanation for the 'no effect' occurrence in HEALTH may lie in the unavailability of application support; this may have nullified the positive effects of a well-developed technical support system. If users are not encouraged or supported in their use of computer applications, then technical support on its own, is unlikely to have a significant impact on actual computer use. Organizational support was shown to have a negative impact on the formation of computer self-efficacy. Although this outcome is inconsistent with that of prior research (Igbaria & Chakrabarti, 1990; Igbaria et al., 1995; Igbaria & Iivari, 1995), this somewhat 'surprising' result corroborates the observations of Compeau and Higgins (1995b). They observed a small but negative relationship between organizational support and computer self-efficacy. A possible explanation for this occurrence is that confidence improves in the presence of requisite skills; hence confident users are less likely to rely on organizational support. On the other hand, users who rely on organizational support may never develop a belief that they are, or can become, capable of performing the task themselves. The presence of a well-developed system of IS support and peer networking may therefore hinder the formation of self-efficacy since someone is usually available to assist with problems and difficulties. If documentation and other self-help aids are generally unavailable, this may also increase the reliance of less sophisticated users on IS support as their 'first and only' option for assistance.
Computer Self-efficacy The survey results have shed some light on the role of computer self-efficacy in respect of computer use and user sophistication. Consistent with expectations, the research findings provided strong support for the positive relationship between computer self-efficacy and user sophistication, and moderate support for the relationship between computer self-efficacy and computer use. These are discussed below: Consistent with expectation, computer self-efficacy was shown to have a strong positive impact on the development of user sophistication. These findings confirm previous research which showed that computer-self-efficacy was significantly related to user sophistication (Munro et al., 1994). This suggests that users with a higher expectation of their own ability to perform will tend to develop greater sophistication, whereas users with a decreased expectation will exhibit a lower level of user sophistication. This is consistent with Social Cognitive Theory which has shown that self-efficacy systematically varies with behavioural change (Bandura, 1982, 1986). Hence, individuals with higher efficacy may be expected to consider themselves able to competently use different computer tools, while those with a lower sense of efficacy are likely to perceive their abilities as being limited to a particular package or computing system (Compeau & Higgins, 1995b). These findings also suggest, that as users becomes more confident in their ability to use computers and to use them successfully, they may become encouraged to further develop their knowledge of computing technology. It would be reasonable to expect such users to actively seek more knowledge on the different computing tools available; to develop a deeper understanding of certain tools and how to use more aspects of these tools; and to endeavour to apply their knowledge and skills to different aspects of the task environments. Computer self-efficacy also played an important role in mediating the effects of factors such as training, computing opportunities, and organizational support on user sophistication. This is also consistent with Social Cognitive Theory which suggests that self-efficacy functions as a mechanism to mediate behavioural change (Bandura, 1986), since the appraisal of self-efficacy is based on factors such as performance accomplishments and verbal persuasion, an analysis of skill level, and an assessment of the task environment (Gist & Mitchell, 1987). This underscores the importance of computer self-efficacy in the development of user sophistication and the need for users to have an accurate perception of their abilities. Social Cognitive Theory also suggests higher levels of self-confidence can influence factors such as the user's effort, perseverance, and self-aid (Bandura, 1977a; 1986) in developing personal sophistication, while minimising
the effect of anxieties. It is therefore suggested that organizations wishing to encourage the development of user sophistication, should consider the implications which computer self-efficacy has for factors such as effort, perseverance, and 'self-aiding' responses to skills development. Organizations should also assist users to develop and improve personal efficacy in a way that accurately reflects their capabilities. Consistent with expectations, the research findings provided some evidence to suggest that individuals with a higher sense of computer self-efficacy tend to use computers more. Conversely, users with a lower sense of selfefficacy would tend to use computers to a lesser extent. These results underline the importance of belief in one's ability, as an influential factor on computer use, and confirm prior research which indicates that computer selfefficacy may have an important impact on computer-related behaviours (Compeau & Higgins, 1995b; Hill et al., 1987; Igbaria et al., 1995; Igbaria & Iivari, 1995).
Computer Use In this study, computer use was conceptualised in terms of the amount of time a user spends using a computer. This included an assessment of frequency of use (i.e., how often the user uses the computer), and actual use (i.e., how much time the user spends using the computer in a typical working day). The predicted effect of computer use on user sophistication is discussed below. Consistent with expectation, computer use was shown to have a direct and positive effect on user sophistication − individuals who used computers more tend to become more sophisticated. This finding confirms previous research which found that users who spend a considerable time using computing tools tend to develop sophistication (Munro et al., 1994; Rivard & Huff, 1984). For example, Munro et al. (1994) found that individuals who had a higher breadth and depth of knowledge tended to use computers more frequently and for longer periods. They also found that finesse was strongly correlated with computer use, which reinforced the idea that creative ability develops from the continued use of computers on the job.
Summary Effects on User Sophistication In the previous section, the discussion focused on the individual effects of the independent variables and the intervening variables. The discussion will now address the summary effects of the determinants on user sophistication, and on the intervening variables (i.e., computer self-efficacy and computer use). Overall, the research findings provided strong support for the collective impact of the determinants of user sophistication; this research was able to explain 63 percent of the observed variance. In particular, the survey results emphasised the importance of computing opportunities, computer self-efficacy, and training in the development of user sophistication. Contrary to expectation, organizational support had a negative impact on user sophistication. In addition, these findings suggest that the effect of the task environment is significantly enhanced through its effect on computer self-efficacy and computer use. Finally, since user sophistication reflects the skills a user can bring to bear on the working environment, it could be said that training and other factors which support and enhance computer-assisted task performance, will aid the development of user sophistication. In general, this research contributes to the current understanding of how user sophistication develops. In particular, the research findings are consistent with the idea that skills and expertise are developed through the acquisition of advanced knowledge and the ability to practice and apply the knowledge gained; the user therefore "gets better with practice". The research findings suggest advanced training as well as, a working environment in which the user is exposed to different tools and is provided with occasions to use these tools and skills will engender skill development. These results also extend our understanding of the importance, and the role, of computer self-efficacy in directly affecting computer-related behavioural outcomes, and in mediating the effects of skill and task-related attributes. Finally, notwithstanding the desire for, and the importance of, user sophistication in the organization, all users do not wish to be, or need to be experts; some of the less sophisticated users may be satisfied with knowing just enough to get by (Cragg & Zinatelli, 1995; Mirani & King, 1994b). In addition the results strongly suggest the
primacy of the relationship between the task environment and the development of user sophistication. This suggests the importance of considering the value and usefulness of skill enhancement to the task environment; sophistication that is beneficial to the organization is that which is applicable in, and beneficial to, the user's task environment. Organizations wishing to develop user sophistication must ensure the task environment is one which encourages, and provides, the requisite training for task performance. It could also facilitate opportunities, through task design and performance enhancement, for the user to practice the skills that have been acquired.
Summary Effects on Computer Use The research findings showed that the independent variables and computer self-efficacy collectively account for 31 percent of the observed variance for computer use. In particular, these findings emphasised the importance of the nature of one's job (i.e., task uncertainty and computing opportunities) on computer use. Although training also had a significant impact on computer use, the research findings implied that the expected impact of training must be considered in light of the user's job. Indeed, the computer functions in the task environment as a tool to support task performance. This is consistent with the idea that effective computer-related training is that which provides the user with the skills and tools required for task performance (Nelson, 1991). The research results also showed that computer self-efficacy had a positive impact on computer use. Contrary to expectation, the research findings indicated that computer self-efficacy did not play a important role in mediating the effects of the independent variables on computer use. Overall, the research findings emphasise the importance of the nature of one's job, requisite training, and user confidence towards encouraging users to make use of available computing tools. Although the research model provided moderate to strong support for these relationships, the explained variance of 31 percent suggests that there are other variables and belief systems which may impact the use of computers (e.g., perceived ease of use and perceived usefulness). These may be a consideration for future research.
Summary Effects on Computer Self-efficacy In general, the research findings provided strong support for the predicted effects of training and computing opportunities on the formation of computer self-efficacy. Within the scope of this research, computing opportunities was shown to have the strongest effect on computer self-efficacy. This suggests that computing opportunities may influence computer self-efficacy through an appraisal of available resources (Gist & Mitchell, 1987) and through performance accomplishment (Bandura, 1986). Consistent with Social Cognitive Theory, training was also shown to have a strong impact on computer self-efficacy (Compeau & Higgins, 1995a; Gist et al., 1988, 1989). Contrary to expectation, organizational support had a small negative effect and task uncertainty had little or no effect. Overall, the research findings underscore the importance of providing users with the opportunity to use computers on the job, and with the training that is consistent with the users' skill requirements for successful task performance. The research findings showed that the independent variables accounted for 34 percent of the observed variance for computer self-efficacy. These research findings are shown to explain more of the variance observed than that of previous research; for example, Compeau and Higgins (1995b) and Igbaria and Iivari (1995). Although these findings suggest some improvement on that of previous research, the variance figures indicate the presence of additional variables (Bandura, 1977a, 1986) to be involved in the formation of computer self-efficacy. This could be considered in future investigations.
SUMMARY OF FINDINGS The research findings corroborate the importance of the independent variables of training, nature of job (i.e., task uncertainty and computing opportunities), computer use, and computer self-efficacy in the development of user
sophistication in this subject HEALTH organisation. In particular, the availability of computing opportunities was shown to have the strongest effect on user sophistication. This research also showed that computer self-efficacy has an important role in mediating the effect of the determinants of user sophistication (particularly, computing opportunities). These findings have provided some useful insights into the determinants of user sophistication and have reinforced the primacy of computer self-efficacy, training, and task characteristics, in the development of user sophistication.
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