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Technical education transfer: perceptions of employee computer technology self-efficacy
Computers in Human Behavior 15 (1999) 161±172
Technical education transfer: perceptions of employee computer technology self-ecacy C.A. Decker* Lincoln Memorial University, 2227 Highway 11 North, Niota, TN 37826, USA
Abstract This study investigated in¯uences on employee self-ecacy of computer technologies resulting from computer training programs that were intended to meet individual and organization objectives for university personnel. Subsequent to training, an assessment of employee computer technology self-ecacy was necessary for determining self-ecacious duration and the usefulness of technical education. A descriptive survey design was used to gather data from a population of 2597 university employees. Results indicated employee computer technology self-ecacy levels remained stable for a 2.5-year period. In addition, select subscales of the variables previous classroom computer training and computer use required on the job predicted computer technology self-ecacy. Frequency of computer use, home computer use, and training responsibility were also noted to in¯uence the transfer of training process as it pertains to computer technology self-ecacy. Interaction relationships were also discovered among certain disciplines of computer use and degree of computer use. Implications of the study are relevant to employee placement and workplace computer education needs. # 1999 Elsevier Science Ltd. All rights reserved. Keywords: Computer technology; Self-ecacy; Training and education transfer
1. Introduction The performances of organizations, according to Heilman and Hornstein (1982), are aected by human forces rather than technical capabilities. For nearly a century, organizations have searched for the means to best achieve their organizational objectives. This quandry is now coupled with the use of progressive computer technology that requires consistent yet scarce human resource pro®ciency for positive organizational results. Historically, decision makers have overlooked the impact of * Tel.: +1-423-337-6410; fax: +1-423-337-6410 (call ®rst); e-mail: [email protected]. 0747-5632/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved. P II: S074 7- 5632( 99) 0001 5-1
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employee self-con®dence or self-ecacy in performance as a component in achieving organizational success. Devastating demands and requirements of computer technology are likely to erode one's con®dence to perform, as well as any desire to undertake any computer-related activities. Employee self-ecacy perceptions of technological advancements are re¯ected in the performance and pro®ciency realized by the organization. Workplace performance and the employee's willingness to learn computer technologies and their related tasks is hindered by low self-ecacy levels (Bandura, 1977; Gist, 1987). Consequently, attention to providing technical workforce preparation that transfers or results in self-ecacious computer technology interaction is a necessity. 2. The foundation of self-ecacy A representative de®nition of self-ecacy is an individual's belief in their ability to perform a particular task (Bandura, 1977, 1982; Gist, 1989). Beginning some 20 years ago, researchers posited that performance was a result of human interactions, mental cognition, and perceived self-ecacy. Although this framework produced performance, the degree of activity varied from pro®cient to reactive. As a result, the level of self-ecacy one achieves through human interactions and mental cognition is the focus believed to generate positive and skilled transfer to work environments. Bandura (1977) stated that information needed to make self-ecacious judgments are obtained by: (1) performance accomplishments or enactive mastery; (2) vicarious learning experiences; (3) verbal persuasion; and (4) physiological arousal. Performance accomplishments make a cognitive register in which self-thought compares the possibilities of future task attainment. Bandura (1982) said: People register notable increases in self-ecacy when their experiences discon®rm misbeliefs about what they fear and when they gain new skills to manage threatening activities. If in the course of completing a task, they discover something that appears intimidating about the undertaking, or suggests limitations to their mode of coping, they register decline in self-ecaciousness despite their successful performance. (pp. 125±126) In the same study, he referred to verbal persuasion as the use of conversation and collaboration to reach a level of self-ecacy. Physiological arousal, conversely, acts as the responsible agent for changing emotions to ®t a self-ecacy judgment mode. Vicarious learning experiences occur by watching and absorbing the struggles and successes of others (Popper & Lipschiz, 1993). Gist (1989) and Gist and Mitchell (1992) noted self-ecacy as the basis for choosing what to do, sustaining amount of eort needed for attainment, and preserving experiences. Bandura (1977) found self-ecacy judgments to result in four types of behavior: (1) performance; (2) coping; (3) arousal; and (4) persistence of situations individuals choose for themselves. Bandura (1982) noted self-ecacy as a determinant of choice behavior because it in¯uences the choice of behavioral
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settings. When individuals recognize coping as inadequate for addressing threatening situations, those situations are avoided. On the other hand, if people feel coping is acceptable, they may participate in that kind of situation. Bandura (1988) wrote that three elements: (1) skills matched with basic competencies; (2) complex skills broken down into subskills; and (3) learning to apply skill competencies and subskills with dierent people and circumstances, result in positive performance. He emphasized that new skills are rarely used for long unless they are applied in work situations which provide an environment of experience for building personal con®dence. Accordingly he said self-ecacy: . . . in dealing with one's environment is not a ®xed act or simply a matter of knowing what to do. Rather, self-ecacy involves a generative capability in which component, cognitive, social, and behavioral skills must be organized into integrated courses of action to serve innumerable purposes . . . Self-ecacy judgments, whether accurate or faulty, in¯uence choice of activities and environmental settings. (Bandura, 1988, pp. 122±123) Bandura (1977, 1982) contended that training programs can in¯uence sources of information that, in turn, result in self-ecacy judgments to reliably measure performance in program objectives. Self-ecacy theory is applicable in numerous settings which require performance or where performance is expected. In this study, self-ecacy is considered within the context of computer technologies. Murphy, Coover, and Owen (1988) de®ned computer self-ecacy as an individual's belief that they can perform a speci®c computer task. Narrowing the de®nition of self-ecacy, Kinzie and Delcourt (1991) recognized ¯uctuating levels of self-ecacy with regard to speci®c technologies and derived the term self-ecacy of computer technologies. They de®ned ecacy of computer technologies as an individual's belief in their ability to use a speci®c computer technology such as word processing, e-mail, and CD-ROM databases. 3. Review of literature Self-ecacy in computer technology transfer research is quite incomplete with regard to the combination of employees, work environments, and their computer use/ technology impact on organizational eectiveness. Existing studies utilize service and sales organization employees such as nurses, clerical, administrative or professional, and sales representatives to determine factors that explain self-ecacy (Chowdhury, 1990; Connell, 1990; Davis, 1994; Earley, 1994; Henry & Stone, 1994; Lee & Gillen, 1989; Parker, 1993). Parker (1993) and Earley (1994) were the sole researchers in addressing factors impacting computer use self-ecacy. Utilizing students and industry employees, some studies recognized computer self-ecacy as a component of user acceptance in e-mail (Minuet, CC: Mail) and gopher information technology (Venkatesh & Davis, 1994, 1999; Yi & Venkatesh, 1996). Venkatesh and Davis (1994) supported hands-on training as a signi®cant dierence in self-ecacy and perceived
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ease of use. E-mail self-ecacy was also the focus of a study using university faculty (Kandies, 1995). Several studies used a variety of university employees and state government employees to analyze how aspects of work aected general self-ecacy (Jex & Gudanowski, 1992; Kennedy, 1993; Latham & Frayne, 1989; Prieto & Altmaier, 1994). Computer self-ecacy and self-ecacy of computer technology studies historically have utilized students as a focal population (Ellen, 1987; Kinzie & Delcourt, 1991; Murphy et al., 1988; Patterson, 1984; Prieto & Altamaier, 1994). 4. Purpose of the study The purpose of this study is to convey results of transfer of training in¯uences on self-ecacy of computer technologies subsequent to training and self-ecacy duration for training usefulness. 5. Rationale for the study With computer technology being a major component in workplace performance, creating technical education and training programs that actually transfer computer technology skills from the classroom to the work environment continues to be a vital focus of organizational productivity. The present study provides educators and industry with information for improving, monitoring, and evaluating their programs. This study will speci®cally suggest technical education instructional techniques required in classroom and performance-based training to ensure positive performance at school and transfer to the workplace. Moreover, the study provides information regarding how individuals should be placed in levels of occupations in order to maximize performance. Last of all, previous studies declare that the amount of time between training and application is an indicator of the degree to which training transfers to on-the-job performance. While the variable time is examined as a performance in¯uence in this study, it is also used to account for the duration of computer technology self-ecacy. The lasting eect or usefulness of trained technology self-ecacy can be an indicator for educators and organizations to improve curriculums, educational initiatives, or provide continuing education for employees. 6. Research objectives Speci®c research objectives addressed by this study were: 1. To determine dierences in self-ecacy of computer technology among university employees with regard to time (between training and survey time), home computer use, previous computer classroom training, computer use required on the job, training responsibilities, and frequency of computer use; and
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2. To determine interactions in self-ecacy of computer technologies among university employees with regard to time, home use, previous computer classroom training, computer use required on the job, training responsibilities, and frequency of computer use. 7. Procedures and methodology A descriptive ex post facto survey design was used to gather data from a population of 2597 university clerical/administrative, technical/maintenance, and faculty employees who had received computer-related training in a 2.5-year period. The employees received this non-mandatory training ranging from word processing to programming in a 4-week period and for one time during the 2.5-year period. A total of 357 responses from a sample of 448 provided an overall response rate of 80%. Dillman (1978) indicated that response rate is determined by dividing the number of questionnaires returned by the number in the sample minus those ineligible. Although the National Education Association Research Division (1960) bulletin indicated a required sample size of 335 to ensure a 95% con®dence level, the researcher felt it prudent for population generalization to have a random sample size of 500. Of the sample size, 52 questionnaires were associated with trainees who were ineligible which resulted in 448 viable assessments. Non-respondents were sent followup letters 1 and 2 weeks after the ®rst mailing culminating with a third follow-up attempt to encourage participation or to gain completions by phone. The `computer self-con®dence assessment' consisted of a background segment for gathering demographic information, and the self-ecacy of computer technologies scale (SCT). Kinzie and Delcourt (1991) developed the SCT scale in response to a lack of instruments available for measuring self-ecacy of computer technologies such as word processing, e-mail, and CD-ROM databases. The scale contained a total of 27 activity items with 10 subscale items related to word processing, 10 items related to e-mail, and seven items related to compact disc (CD-ROM) databases. The scale was validated by a pilot study utilizing a panel of 17 experts consisting of computer technology instructors, measurement experts, educational consultants, and graduate students. A critique of the questionnaire was also provided by a university instrument review committee. After the pilot study, a Cronbach alpha of 0.84 was determined. Kinzie and Delcourt ®rst administered the questionnaire to 313 undergraduate and graduate respondents across the USA at major universities. Items such as previous computer use and previous computer training were used for demographic information about the respondents. Study participants rated their degree of con®dence by strongly disagreeing or strongly agreeing with the con®dence statements on a 4-point Likert-type scale (1=strongly disagree, 4=strongly agree). Robust validation procedures and previous applicability for studying a broadly skilled population regarding general computer technology self-ecacy resulted in the deemed appropriateness of the questionnaire for this workplace-based study. Similar to Kinzie and Delcourt, this study also considered other factors such as previous computer use and previous computer training as contributors to an individual's
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general computer technology self-ecacy in order to posit additional types of computer awareness that can present enhancements to workplace performance. The `background information' section of the assessment asked the employees for the independent variables of previous classroom computer training (database management, e.g. Dbase, AskSam; word processing, e.g. WordPerfect; statistical processing, e.g. SPSSX, SAS; spreadsheets, e.g. Lotus, Excel; programming, e.g. Basic, Cobol; educational software, e.g. Teaching Typing), type of computer use required on the job (database management, e.g. Dbase, AskSam; word processing, e.g. WordPerfect; statistical processing, e.g. SPSSX, SAS; spreadsheets, e.g. Lotus, Excel; programming, e.g. Basic, Cobol; educational software, e.g. Teaching Typing), home computer use, frequency of computer use, and training responsibilities. The examples provided for the variables of previous computer training, and computer use required on the job were considered recognizeable to respondents and associative with applications in use by the organization. The independent variable of time was provided by the Institution. After the computer self-con®dence assessments were collected and compiled, data analyses were performed using the UNIX mainframe computer system. The speci®c statistical analysis was performed using the SAS1 (SAS Institute, 1985) statistical software residing on the UNIX system. Multiple analysis of variance (MANOVA) was used to best determine signi®cant dierences ( p<0.05) of time, home use, previous computer classroom training, computer use required on the job, training responsibilities, and frequency of computer use, on self-ecacy of computer technologies. The Wilks' lambda multiple comparison follow-up procedure was applied on each test of the independent variables on the dependent variable to determine if signi®cant mean dierences occurred. For signi®cant dierences found by the Wilks' lambda, the Tukey honestly signi®cant dierence (HSD) post hoc test was used to make all pairwise comparisons among the independent variable subscales. The dependent variable was the SCT scores while speci®c independent variables considered were time (between training and survey time; 0±6 months, 6 months±1 year, 1±1.5 years, 1.5±2 years, 2±2.5 years), home use (yes or no), previous classroom computer training (database management, word processing, statistical processing, spreadsheets, programming, educational software), computer use required on the job (database management, word processing, statistical processing, spreadsheets, programming, educational software), frequency of computer use (always, frequently, seldom, never), and training responsibilities (yes or no), respectively. 8. Results In Table 1, the MANOVA disclosed signi®cant dierences in self-ecacy of computer technologies of university employees with regard to home computer use, previous classroom computer training (database management, statistical processing), computer use required on the job (database management, statistical processing, programming, educational software), frequency of computer use, and training
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Table 1 Multivariate analysis of variance for self-ecacy of computer technologies (SCT) scores of respondents classi®ed by independent variables df
SS
F
Pr>F
SCT Time Home computer use
4 1
401.670 5150.388
0.29 15.68
0.8829 0.0001*
Previous classroom computer training Database management Word processing Statistical processing Spreadsheets Programming Educational software
1 1 1 1 1 1
5531.120 315.771 1064.938 544.999 626.512 403.057
20.39 1.16 3.93 2.01 2.31 1.49
0.0001* 0.2814 0.0484* 0.1573 0.1296 0.2238
Computer use required on the job Database management Word processing Statistical processing Spreadsheets Programming Educational software Frequency of use Training
1 1 1 1 1 1 3 1
1730.186 27.069 1380.465 870.975 2941.893 1549.418 28 909.943 1987.332
6.75 0.11 5.38 3.40 11.48 6.04 36.94 5.88
0.0098* 0.7454 0.0210* 0.0662 0.0008* 0.0145* 0.0001* 0.0158*
* p<0.05.
responsibilities. There was no signi®cant dierence in SCT scores among university employees with respect to time between training and survey time. The Wilks' lambda multiple comparison follow-up procedures, as shown in Table 2, revealed signi®cant dierences in self-ecacy of computer technologies in university employees with respect to home computer use, previous classroom computer training (database management), computer use required on the job (database management, spreadsheets, programming, educational software), frequency of computer use, and training responsibilities. Tukey's HSD all pairwise procedure, as shown in Table 3, revealed Tukey HSD post hoc signi®cant dierences in SCT mean scores among those participants using computers always and those using computers frequently, never, and seldom. Other signi®cant dierences were noted in SCT mean scores between those using computers frequently and those using computers seldomly and never. Signi®cant interactions, as shown in Table 4, were among programming required on the job and frequency of computer use, home computer use, and training responsibilities. 9. Conclusions and discussion Results signify in¯uences to be considered when designing technical education or training for transferable performance to the workplace. Previous classroom computer
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Table 2 Multivariate analysis of variance for mean scores of respondents classi®ed by independent variables Wilks' lambda
df
F
Pr>F
Time Home use
0.9779 0.9500
8, 686 2, 344
0.9592 9.0429
0.4669 0.0001*
Previous classroom computer training Database management Word processing Statistical processing Spreadsheets Programming Educational software
0.9326 0.9943 0.9875 0.9889 0.9904 0.9943
2, 2, 2, 2, 2, 2,
311 311 311 311 311 311
11.2333 0.8977 1.9719 1.7858 1.5085 0.8871
0.0001* 0.4086 0.1409 0.1899 0.2229 0.4129
Computer use required on the job Database management Word processing Statistical processing Spreadsheets Programming Educational software Frequency of use Training
0.9377 0.9990 0.9822 0.9317 0.9647 0.9798 0.6791 0.9689
2, 2, 2, 2, 2, 2, 6, 2,
315 315 315 315 315 315 684 340
10.4716 0.1556 2.8538 11.5449 5.7572 3.2515 24.3380 5.4529
0.0001* 0.8559 0.0591 0.0001* 0.0035* 0.0400* 0.0001* 0.0047*
* p<0.05. Table 3 Tukey's honestly signi®cant dierence (HSD)a procedure (simultaneous con®dence intervals) for comparisons of subscale means for variable of frequency of computer use Frequency of use subscales SCT b A±F A±N A±S F±N F±S N±S
Lower con®dence limit
Mean
Upper con®dence limit
6.174 13.480 24.088 2.431 12.973 ÿ11.981
10.914 28.530 34.123 17.616 23.209 5.592
15.654* 43.581* 44.158* 32.802* 33.444* 23.165
SCT, self-ecacy of computer technologies; frequency of computer use: A, always; F, frequently; S, seldom; N, never. * p<0.05. a Controlling for Type I experimentwise error rate at 0.05 level of signi®cance. b Group means are signi®cantly dierent.
training (database management, statistical processing), computer use required on the job (database management, statistical processing, programming, educational software), frequency of computer use, home use, and training responsibilities are denoted as forces used to explain an individual's level of self-ecacy from previous education and training. Previous classroom computer training in database management and statistical processing can contribute to opulent transfer of technical
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Table 4 Multivariate analysis of variance for mean scores of respondents classi®ed by signi®cant main eect and subscale interactions
DMDM2 DMS2 DM2S2 DMP2 DM2P2 S2P2 DMES2 DM2ES2 P2ES2 DMFrequse DM2Frequse S2Frequse P2Frequse ES2Frequse DMHome use DM2Home use S2Home use P2Home use ES2Home use FrequseHome use DMTrain DM2Train S2Train P2Train ES2Train FrequseTrain Home useTrain
Wilks' lambda
df
F
Pr>F
0.9997 0.9996 0.9908 0.9747 0.9990 0.9943 0.9925 0.9822 0.9904 0.9973 0.9998 0.9988 0.9546 0.9875 0.9837 0.9907 0.9971 0.9639 0.9993 0.9876 0.9902 0.9813 0.9894 0.9695 0.9842 0.9849 0.9979
2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225
0.0307 0.0449 1.0498 2.9161 0.1110 0.6460 0.8555 2.0402 1.0916 0.3092 0.0198 0.1328 5.3548 1.4265 1.8661 1.0608 0.3244 4.2165 0.0767 1.4099 1.1173 2.1393 1.2014 3.5343 1.8032 1.7258 0.2354
0.9698 0.9561 0.3517 0.0562 0.8949 0.5251 0.4264 0.1324 0.3375 0.7343 0.9804 0.8757 0.0053* 0.2423 0.1571 0.3479 0.7233 0.0159* 0.9262 0.2463 0.3290 0.1201 0.3027 0.0308* 0.1671 0.1804 0.7904
DM and DM2, database management (type of previous classroom computer training and computer use required on the job, respectively); S2, spreadsheets (type of computer use required on the job); P2, programming (type of computer use required on the job); ES2, educational software (type of computer use required on the job); Frequse, frequency of computer use; Train, training responsibility; * p<0.05. Note. Missing information due to non-responses eliminated analysis of interactions DMSP, SPSP2, SPP2, SPES2, S2ES2, SPTrain.
workplace performance. Tasks within database management and statistical processing could be responsible for higher self-ecacious performance. Moreover, employees having database management, statistical processing, programming, and educational software use required on the job testify to the need for training and education parallels to job performance. Not only is it imperative that technical education compliment job requirements but consideration should be given to providing employees with the opportunity to apply such technical skills with the possibility of attaining higher pro®ciency. The use or challenge of these technical skills, although untrained, may produce motivated cognition to perform. Additionally, frequency of computer use is in¯uential to higher performance in computer technology. Employees reporting consistent use of computers maintain
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more steady and productive performance that those who use computers on a more sporadic basis. Home use, too, provides individuals with more con®dence to perform. Using computers at home allows more time for use and opportunity to improve one's skill level. Providing employees with more opportunities to expand their skill levels and raising the technological requirements in the workplace is likely to induce this essential degree of use. Likewise, employees given training responsibilities with computer technology increase their own use and level of skill and, thus, are more productive. This increased desire to perform can be attributed to authority recognition as well as the simple opportunity to use more technology on the job. Being more self-ecacious in computer technology requires some combination of home use, previous computer training (database management), computer use on the job (database management, spreadsheets, programming, educational software), frequency of use, and training responsibilities. Individuals with the opportunity to consistently use computers at home and work and who have had previous training in applications similar to database management can apply those applications to the job. Moreover, they are more apt to increase those skills through more technology application on the job. Also, employees having programming required on the job have more occasions to enhance their abilities through home use, training responsibilities, and overall frequency of use. Those performing programming on the job are more con®dent in performance due to increased use in a variety of circumstances. While providing a short distance between the time of training and performance has long been considered a necessary ingredient to transfer, this study indicates it as insigni®cant. According to the results and utilizing a 2.5-year time span, time distance did not instill self-ecacious performance with technology. Self-ecacy of computer technologies did not ¯uctuate regardless of how long training was received. It appears that with training as a prerequisite or to some existence, employees continue to be self-ecacious for a 2.5-year period. Therefore, the duration and usefulness of this training for technology self-ecacy provided in this study is 2.5 years. 10. Recommendations and implications This study re-emphasizes the necessitated role of organizations in equipping any and all human beings with technical workplace skills. Organizations that fail to provide employees with occasions to increase productivity will be negatively impacted by high turnover, more employee expenses, lower quality, and lost market share. Today, education and training programs must prove return to organizational worth. By monitoring the self-ecacy of such programs, educators can provide instruction which meets the challenges of employees and renders the productivity desired by organizations. Because time did not impact self-ecacy in computer technology, educators can assume that quality instruction remains self-ecacious and produces pro®cient performance for a 2.5-year period. However, prior to the this time frame, educators should be prepared to upgrade computer training and to implement a
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continuing computer education program to maintain con®dence and consistency in performance as technology changes. The research does recognize that there may be other in¯uences that contribute to technology self-ecacy and accentuates the need for research in those areas. For example, an individual's job may be indicative of how con®dent they are with the applications they use. Job components should be addressed to determine if responsibilities match with education. Performance in terms of self-ecacy should be assessed to determine what components of the job or education contribute to selfecacy. The research also recommends that educators search for the individual task within the training that would produce higher self-ecacy levels. As with database management and programming, there may be aspects of this training that can be introduced on other courses which will ensure technology self-ecacy. Curriculums, based upon con®dence-producing minute particles of technological interaction, can be prepared to enhance and extend self-ecacy and establish a employee comfortable con®dence zone for performance. Organizations, too, must be willing to extend the skill level of their employees through greater use and challenge. Organizations must make greater use of techniques such as delegation so that employees can be better challenged with use opportunities, thereby increasing their skills in present and unknown areas of technology. This can imply that other methods such as job rotation and placement in a variety of jobs produces higher self-ecacious performance. Giving training responsibilities is one proven method of increasing personal and co-worker performance. This study infers an even greater need for organizations to consider the impact of training and education on their overall performance. Since time distance was not a factor in producing higher levels of self-ecacy, organizations should seek to continuously improve their employee technical education programs. Without further study, it is possible to imply that our technical education programs remain selfecaciously stable or they never produced self-ecacious behavior at all.
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Earley, P. C. (1994). Self or group? Cultural eects of training on self-ecacy and performance. Administrative Science Quarterly, 39, 89±117. Ellen, P. S. (1987). The impact of self-ecacy and performance satisfaction on resistance to change (Doctoral dissertation, University of South Carolina, 1987). Dissertation Abstracts International, 48, 2106. Gist, M. E. (1987). Self-ecacy: implications for organizational behavior and human resource management. Academy of Management Review, 12, 472±485. Gist, M. E. (1989). The in¯uence of training method on self-ecacy and idea generation among managers. Personnel Psychology, 42, 787±805. Gist, M. E., & Mitchell, T. R. (1992). Self-ecacy: a theoretical analysis: Of its determinants and malleability. Academy of Management Review, 17(2), 183±211. Heilman, M. E., & Hornstein, H. A. (1982). Managing human forces in organizations. Homewood, IL: Irwin. Henry, J. W., & Stone, R. W. (1994). A structural equation model of end-user satisfaction with a computer-based medical information system. Information Resources Management Journal, 7(3), 21±33. Jex, S. M., & Gudanowski, D. M. (1992). Ecacy beliefs and work stress: an exploratory study. Journal of Organizational Behavior, 13(5), 509±517. Kandies, J. T. (1995). Electronic mail: attitudes, self-ecacy, and eective communication (Doctoral dissertation, Virginia Polytechnic Institute and State University, 1995). Dissertation Abstracts International, 55, 2255. Kennedy, D. M. (1993). The relationship between faculty members' level of computer use and computer self-ecacy (Doctoral dissertation, Eastern Michigan University, 1993). Dissertation Abstracts International, 31, 516. Kinzie, M. B., & Delcourt, M. A. B. (1991). Computer technologies in teacher education: the measurement of attitudes and self-ecacy (ERIC Document Reproduction Service No. ED 331 891). Chicago, IL: American Educational Research Association. Latham, G. P., & Frayne, C. A. (1989). Self-management training for increasing job attendance: a followup and a replication. Journal of Applied Psychology, 74(3), 411±416. Lee, C., & Gillen, D. J. (1989). Relationship of type A behavior pattern: self-ecacy perceptions on sales performance. Journal of Organizational Behavior, 10(1), 75±81. Murphy, C. A., Coover, D., & Owen, S. V. (1988). Assessment of computer self-ecacy: instrument development and validation (ERIC Document Reproduction Service No. ED 307 317). New Orleans, LA: National Council on Measurement in Education. National Education Association Research Division (1960). Table for determining sample size from a given population. Research Bulletion, 38, 99 Parker, L. E. (1993). When to ®x it and when to leave: relationships among perceived control, self-ecacy, dissent, and exit. Journal of Applied Psychology, 78, 949±959. Patterson, D. P. (1984). The eects of certain verbal persuasion procedures on self-ecacy (Doctoral dissertation, Arizona State University, 1984). Dissertation Abstracts International, 45, 727. Popper, M., & Lipschiz, R. (1993). Putting leadership theory to work: a conceptual framework for theory based leadership development. Leadership and Organization Development Journal, 14(7), 23±27. Prieto, L. R., & Altmaier, E. M. (1994). The relationship of prior training and previous teaching experience to self-ecacy among graduate teaching assistants. Research in Higher Education, 35(4), 481±497. SAS Institute (1985). SAS/STAT guide for personal computers, version 6 edition. Cary, NC: SAS Institute. Venkatesh, V., & Davis, F. D. (1994). Modeling the determinants of perceived ease of use. [On-line]. International Conference on Information Systems. Abstract from: http://webfoot.csom.umn.edu/faculty/ phds/venki/cse2.htm Venkatesh, V., & Davis, F. D. (1999). A model of the antecedents of perceived ease of use: development and test [On-line]. Decision Sciences. Available: http://www.CSE1ai.htm (forthcoming). Yi, M. Y., & Venkatesh, V. (1996). Role of computer self-ecacy in predicting user acceptance and use of information technology. [On-line]. Available: http://hsb.baylor.edu/ramsower/ais.ac96/papers/mun.htm
Technical education transfer: perceptions of employee computer technology self-ecacy C.A. Decker* Lincoln Memorial University, 2227 Highway 11 North, Niota, TN 37826, USA
Abstract This study investigated in¯uences on employee self-ecacy of computer technologies resulting from computer training programs that were intended to meet individual and organization objectives for university personnel. Subsequent to training, an assessment of employee computer technology self-ecacy was necessary for determining self-ecacious duration and the usefulness of technical education. A descriptive survey design was used to gather data from a population of 2597 university employees. Results indicated employee computer technology self-ecacy levels remained stable for a 2.5-year period. In addition, select subscales of the variables previous classroom computer training and computer use required on the job predicted computer technology self-ecacy. Frequency of computer use, home computer use, and training responsibility were also noted to in¯uence the transfer of training process as it pertains to computer technology self-ecacy. Interaction relationships were also discovered among certain disciplines of computer use and degree of computer use. Implications of the study are relevant to employee placement and workplace computer education needs. # 1999 Elsevier Science Ltd. All rights reserved. Keywords: Computer technology; Self-ecacy; Training and education transfer
1. Introduction The performances of organizations, according to Heilman and Hornstein (1982), are aected by human forces rather than technical capabilities. For nearly a century, organizations have searched for the means to best achieve their organizational objectives. This quandry is now coupled with the use of progressive computer technology that requires consistent yet scarce human resource pro®ciency for positive organizational results. Historically, decision makers have overlooked the impact of * Tel.: +1-423-337-6410; fax: +1-423-337-6410 (call ®rst); e-mail: [email protected]. 0747-5632/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved. P II: S074 7- 5632( 99) 0001 5-1
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employee self-con®dence or self-ecacy in performance as a component in achieving organizational success. Devastating demands and requirements of computer technology are likely to erode one's con®dence to perform, as well as any desire to undertake any computer-related activities. Employee self-ecacy perceptions of technological advancements are re¯ected in the performance and pro®ciency realized by the organization. Workplace performance and the employee's willingness to learn computer technologies and their related tasks is hindered by low self-ecacy levels (Bandura, 1977; Gist, 1987). Consequently, attention to providing technical workforce preparation that transfers or results in self-ecacious computer technology interaction is a necessity. 2. The foundation of self-ecacy A representative de®nition of self-ecacy is an individual's belief in their ability to perform a particular task (Bandura, 1977, 1982; Gist, 1989). Beginning some 20 years ago, researchers posited that performance was a result of human interactions, mental cognition, and perceived self-ecacy. Although this framework produced performance, the degree of activity varied from pro®cient to reactive. As a result, the level of self-ecacy one achieves through human interactions and mental cognition is the focus believed to generate positive and skilled transfer to work environments. Bandura (1977) stated that information needed to make self-ecacious judgments are obtained by: (1) performance accomplishments or enactive mastery; (2) vicarious learning experiences; (3) verbal persuasion; and (4) physiological arousal. Performance accomplishments make a cognitive register in which self-thought compares the possibilities of future task attainment. Bandura (1982) said: People register notable increases in self-ecacy when their experiences discon®rm misbeliefs about what they fear and when they gain new skills to manage threatening activities. If in the course of completing a task, they discover something that appears intimidating about the undertaking, or suggests limitations to their mode of coping, they register decline in self-ecaciousness despite their successful performance. (pp. 125±126) In the same study, he referred to verbal persuasion as the use of conversation and collaboration to reach a level of self-ecacy. Physiological arousal, conversely, acts as the responsible agent for changing emotions to ®t a self-ecacy judgment mode. Vicarious learning experiences occur by watching and absorbing the struggles and successes of others (Popper & Lipschiz, 1993). Gist (1989) and Gist and Mitchell (1992) noted self-ecacy as the basis for choosing what to do, sustaining amount of eort needed for attainment, and preserving experiences. Bandura (1977) found self-ecacy judgments to result in four types of behavior: (1) performance; (2) coping; (3) arousal; and (4) persistence of situations individuals choose for themselves. Bandura (1982) noted self-ecacy as a determinant of choice behavior because it in¯uences the choice of behavioral
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settings. When individuals recognize coping as inadequate for addressing threatening situations, those situations are avoided. On the other hand, if people feel coping is acceptable, they may participate in that kind of situation. Bandura (1988) wrote that three elements: (1) skills matched with basic competencies; (2) complex skills broken down into subskills; and (3) learning to apply skill competencies and subskills with dierent people and circumstances, result in positive performance. He emphasized that new skills are rarely used for long unless they are applied in work situations which provide an environment of experience for building personal con®dence. Accordingly he said self-ecacy: . . . in dealing with one's environment is not a ®xed act or simply a matter of knowing what to do. Rather, self-ecacy involves a generative capability in which component, cognitive, social, and behavioral skills must be organized into integrated courses of action to serve innumerable purposes . . . Self-ecacy judgments, whether accurate or faulty, in¯uence choice of activities and environmental settings. (Bandura, 1988, pp. 122±123) Bandura (1977, 1982) contended that training programs can in¯uence sources of information that, in turn, result in self-ecacy judgments to reliably measure performance in program objectives. Self-ecacy theory is applicable in numerous settings which require performance or where performance is expected. In this study, self-ecacy is considered within the context of computer technologies. Murphy, Coover, and Owen (1988) de®ned computer self-ecacy as an individual's belief that they can perform a speci®c computer task. Narrowing the de®nition of self-ecacy, Kinzie and Delcourt (1991) recognized ¯uctuating levels of self-ecacy with regard to speci®c technologies and derived the term self-ecacy of computer technologies. They de®ned ecacy of computer technologies as an individual's belief in their ability to use a speci®c computer technology such as word processing, e-mail, and CD-ROM databases. 3. Review of literature Self-ecacy in computer technology transfer research is quite incomplete with regard to the combination of employees, work environments, and their computer use/ technology impact on organizational eectiveness. Existing studies utilize service and sales organization employees such as nurses, clerical, administrative or professional, and sales representatives to determine factors that explain self-ecacy (Chowdhury, 1990; Connell, 1990; Davis, 1994; Earley, 1994; Henry & Stone, 1994; Lee & Gillen, 1989; Parker, 1993). Parker (1993) and Earley (1994) were the sole researchers in addressing factors impacting computer use self-ecacy. Utilizing students and industry employees, some studies recognized computer self-ecacy as a component of user acceptance in e-mail (Minuet, CC: Mail) and gopher information technology (Venkatesh & Davis, 1994, 1999; Yi & Venkatesh, 1996). Venkatesh and Davis (1994) supported hands-on training as a signi®cant dierence in self-ecacy and perceived
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ease of use. E-mail self-ecacy was also the focus of a study using university faculty (Kandies, 1995). Several studies used a variety of university employees and state government employees to analyze how aspects of work aected general self-ecacy (Jex & Gudanowski, 1992; Kennedy, 1993; Latham & Frayne, 1989; Prieto & Altmaier, 1994). Computer self-ecacy and self-ecacy of computer technology studies historically have utilized students as a focal population (Ellen, 1987; Kinzie & Delcourt, 1991; Murphy et al., 1988; Patterson, 1984; Prieto & Altamaier, 1994). 4. Purpose of the study The purpose of this study is to convey results of transfer of training in¯uences on self-ecacy of computer technologies subsequent to training and self-ecacy duration for training usefulness. 5. Rationale for the study With computer technology being a major component in workplace performance, creating technical education and training programs that actually transfer computer technology skills from the classroom to the work environment continues to be a vital focus of organizational productivity. The present study provides educators and industry with information for improving, monitoring, and evaluating their programs. This study will speci®cally suggest technical education instructional techniques required in classroom and performance-based training to ensure positive performance at school and transfer to the workplace. Moreover, the study provides information regarding how individuals should be placed in levels of occupations in order to maximize performance. Last of all, previous studies declare that the amount of time between training and application is an indicator of the degree to which training transfers to on-the-job performance. While the variable time is examined as a performance in¯uence in this study, it is also used to account for the duration of computer technology self-ecacy. The lasting eect or usefulness of trained technology self-ecacy can be an indicator for educators and organizations to improve curriculums, educational initiatives, or provide continuing education for employees. 6. Research objectives Speci®c research objectives addressed by this study were: 1. To determine dierences in self-ecacy of computer technology among university employees with regard to time (between training and survey time), home computer use, previous computer classroom training, computer use required on the job, training responsibilities, and frequency of computer use; and
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2. To determine interactions in self-ecacy of computer technologies among university employees with regard to time, home use, previous computer classroom training, computer use required on the job, training responsibilities, and frequency of computer use. 7. Procedures and methodology A descriptive ex post facto survey design was used to gather data from a population of 2597 university clerical/administrative, technical/maintenance, and faculty employees who had received computer-related training in a 2.5-year period. The employees received this non-mandatory training ranging from word processing to programming in a 4-week period and for one time during the 2.5-year period. A total of 357 responses from a sample of 448 provided an overall response rate of 80%. Dillman (1978) indicated that response rate is determined by dividing the number of questionnaires returned by the number in the sample minus those ineligible. Although the National Education Association Research Division (1960) bulletin indicated a required sample size of 335 to ensure a 95% con®dence level, the researcher felt it prudent for population generalization to have a random sample size of 500. Of the sample size, 52 questionnaires were associated with trainees who were ineligible which resulted in 448 viable assessments. Non-respondents were sent followup letters 1 and 2 weeks after the ®rst mailing culminating with a third follow-up attempt to encourage participation or to gain completions by phone. The `computer self-con®dence assessment' consisted of a background segment for gathering demographic information, and the self-ecacy of computer technologies scale (SCT). Kinzie and Delcourt (1991) developed the SCT scale in response to a lack of instruments available for measuring self-ecacy of computer technologies such as word processing, e-mail, and CD-ROM databases. The scale contained a total of 27 activity items with 10 subscale items related to word processing, 10 items related to e-mail, and seven items related to compact disc (CD-ROM) databases. The scale was validated by a pilot study utilizing a panel of 17 experts consisting of computer technology instructors, measurement experts, educational consultants, and graduate students. A critique of the questionnaire was also provided by a university instrument review committee. After the pilot study, a Cronbach alpha of 0.84 was determined. Kinzie and Delcourt ®rst administered the questionnaire to 313 undergraduate and graduate respondents across the USA at major universities. Items such as previous computer use and previous computer training were used for demographic information about the respondents. Study participants rated their degree of con®dence by strongly disagreeing or strongly agreeing with the con®dence statements on a 4-point Likert-type scale (1=strongly disagree, 4=strongly agree). Robust validation procedures and previous applicability for studying a broadly skilled population regarding general computer technology self-ecacy resulted in the deemed appropriateness of the questionnaire for this workplace-based study. Similar to Kinzie and Delcourt, this study also considered other factors such as previous computer use and previous computer training as contributors to an individual's
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general computer technology self-ecacy in order to posit additional types of computer awareness that can present enhancements to workplace performance. The `background information' section of the assessment asked the employees for the independent variables of previous classroom computer training (database management, e.g. Dbase, AskSam; word processing, e.g. WordPerfect; statistical processing, e.g. SPSSX, SAS; spreadsheets, e.g. Lotus, Excel; programming, e.g. Basic, Cobol; educational software, e.g. Teaching Typing), type of computer use required on the job (database management, e.g. Dbase, AskSam; word processing, e.g. WordPerfect; statistical processing, e.g. SPSSX, SAS; spreadsheets, e.g. Lotus, Excel; programming, e.g. Basic, Cobol; educational software, e.g. Teaching Typing), home computer use, frequency of computer use, and training responsibilities. The examples provided for the variables of previous computer training, and computer use required on the job were considered recognizeable to respondents and associative with applications in use by the organization. The independent variable of time was provided by the Institution. After the computer self-con®dence assessments were collected and compiled, data analyses were performed using the UNIX mainframe computer system. The speci®c statistical analysis was performed using the SAS1 (SAS Institute, 1985) statistical software residing on the UNIX system. Multiple analysis of variance (MANOVA) was used to best determine signi®cant dierences ( p<0.05) of time, home use, previous computer classroom training, computer use required on the job, training responsibilities, and frequency of computer use, on self-ecacy of computer technologies. The Wilks' lambda multiple comparison follow-up procedure was applied on each test of the independent variables on the dependent variable to determine if signi®cant mean dierences occurred. For signi®cant dierences found by the Wilks' lambda, the Tukey honestly signi®cant dierence (HSD) post hoc test was used to make all pairwise comparisons among the independent variable subscales. The dependent variable was the SCT scores while speci®c independent variables considered were time (between training and survey time; 0±6 months, 6 months±1 year, 1±1.5 years, 1.5±2 years, 2±2.5 years), home use (yes or no), previous classroom computer training (database management, word processing, statistical processing, spreadsheets, programming, educational software), computer use required on the job (database management, word processing, statistical processing, spreadsheets, programming, educational software), frequency of computer use (always, frequently, seldom, never), and training responsibilities (yes or no), respectively. 8. Results In Table 1, the MANOVA disclosed signi®cant dierences in self-ecacy of computer technologies of university employees with regard to home computer use, previous classroom computer training (database management, statistical processing), computer use required on the job (database management, statistical processing, programming, educational software), frequency of computer use, and training
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Table 1 Multivariate analysis of variance for self-ecacy of computer technologies (SCT) scores of respondents classi®ed by independent variables df
SS
F
Pr>F
SCT Time Home computer use
4 1
401.670 5150.388
0.29 15.68
0.8829 0.0001*
Previous classroom computer training Database management Word processing Statistical processing Spreadsheets Programming Educational software
1 1 1 1 1 1
5531.120 315.771 1064.938 544.999 626.512 403.057
20.39 1.16 3.93 2.01 2.31 1.49
0.0001* 0.2814 0.0484* 0.1573 0.1296 0.2238
Computer use required on the job Database management Word processing Statistical processing Spreadsheets Programming Educational software Frequency of use Training
1 1 1 1 1 1 3 1
1730.186 27.069 1380.465 870.975 2941.893 1549.418 28 909.943 1987.332
6.75 0.11 5.38 3.40 11.48 6.04 36.94 5.88
0.0098* 0.7454 0.0210* 0.0662 0.0008* 0.0145* 0.0001* 0.0158*
* p<0.05.
responsibilities. There was no signi®cant dierence in SCT scores among university employees with respect to time between training and survey time. The Wilks' lambda multiple comparison follow-up procedures, as shown in Table 2, revealed signi®cant dierences in self-ecacy of computer technologies in university employees with respect to home computer use, previous classroom computer training (database management), computer use required on the job (database management, spreadsheets, programming, educational software), frequency of computer use, and training responsibilities. Tukey's HSD all pairwise procedure, as shown in Table 3, revealed Tukey HSD post hoc signi®cant dierences in SCT mean scores among those participants using computers always and those using computers frequently, never, and seldom. Other signi®cant dierences were noted in SCT mean scores between those using computers frequently and those using computers seldomly and never. Signi®cant interactions, as shown in Table 4, were among programming required on the job and frequency of computer use, home computer use, and training responsibilities. 9. Conclusions and discussion Results signify in¯uences to be considered when designing technical education or training for transferable performance to the workplace. Previous classroom computer
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Table 2 Multivariate analysis of variance for mean scores of respondents classi®ed by independent variables Wilks' lambda
df
F
Pr>F
Time Home use
0.9779 0.9500
8, 686 2, 344
0.9592 9.0429
0.4669 0.0001*
Previous classroom computer training Database management Word processing Statistical processing Spreadsheets Programming Educational software
0.9326 0.9943 0.9875 0.9889 0.9904 0.9943
2, 2, 2, 2, 2, 2,
311 311 311 311 311 311
11.2333 0.8977 1.9719 1.7858 1.5085 0.8871
0.0001* 0.4086 0.1409 0.1899 0.2229 0.4129
Computer use required on the job Database management Word processing Statistical processing Spreadsheets Programming Educational software Frequency of use Training
0.9377 0.9990 0.9822 0.9317 0.9647 0.9798 0.6791 0.9689
2, 2, 2, 2, 2, 2, 6, 2,
315 315 315 315 315 315 684 340
10.4716 0.1556 2.8538 11.5449 5.7572 3.2515 24.3380 5.4529
0.0001* 0.8559 0.0591 0.0001* 0.0035* 0.0400* 0.0001* 0.0047*
* p<0.05. Table 3 Tukey's honestly signi®cant dierence (HSD)a procedure (simultaneous con®dence intervals) for comparisons of subscale means for variable of frequency of computer use Frequency of use subscales SCT b A±F A±N A±S F±N F±S N±S
Lower con®dence limit
Mean
Upper con®dence limit
6.174 13.480 24.088 2.431 12.973 ÿ11.981
10.914 28.530 34.123 17.616 23.209 5.592
15.654* 43.581* 44.158* 32.802* 33.444* 23.165
SCT, self-ecacy of computer technologies; frequency of computer use: A, always; F, frequently; S, seldom; N, never. * p<0.05. a Controlling for Type I experimentwise error rate at 0.05 level of signi®cance. b Group means are signi®cantly dierent.
training (database management, statistical processing), computer use required on the job (database management, statistical processing, programming, educational software), frequency of computer use, home use, and training responsibilities are denoted as forces used to explain an individual's level of self-ecacy from previous education and training. Previous classroom computer training in database management and statistical processing can contribute to opulent transfer of technical
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Table 4 Multivariate analysis of variance for mean scores of respondents classi®ed by signi®cant main eect and subscale interactions
DMDM2 DMS2 DM2S2 DMP2 DM2P2 S2P2 DMES2 DM2ES2 P2ES2 DMFrequse DM2Frequse S2Frequse P2Frequse ES2Frequse DMHome use DM2Home use S2Home use P2Home use ES2Home use FrequseHome use DMTrain DM2Train S2Train P2Train ES2Train FrequseTrain Home useTrain
Wilks' lambda
df
F
Pr>F
0.9997 0.9996 0.9908 0.9747 0.9990 0.9943 0.9925 0.9822 0.9904 0.9973 0.9998 0.9988 0.9546 0.9875 0.9837 0.9907 0.9971 0.9639 0.9993 0.9876 0.9902 0.9813 0.9894 0.9695 0.9842 0.9849 0.9979
2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225 2, 225
0.0307 0.0449 1.0498 2.9161 0.1110 0.6460 0.8555 2.0402 1.0916 0.3092 0.0198 0.1328 5.3548 1.4265 1.8661 1.0608 0.3244 4.2165 0.0767 1.4099 1.1173 2.1393 1.2014 3.5343 1.8032 1.7258 0.2354
0.9698 0.9561 0.3517 0.0562 0.8949 0.5251 0.4264 0.1324 0.3375 0.7343 0.9804 0.8757 0.0053* 0.2423 0.1571 0.3479 0.7233 0.0159* 0.9262 0.2463 0.3290 0.1201 0.3027 0.0308* 0.1671 0.1804 0.7904
DM and DM2, database management (type of previous classroom computer training and computer use required on the job, respectively); S2, spreadsheets (type of computer use required on the job); P2, programming (type of computer use required on the job); ES2, educational software (type of computer use required on the job); Frequse, frequency of computer use; Train, training responsibility; * p<0.05. Note. Missing information due to non-responses eliminated analysis of interactions DMSP, SPSP2, SPP2, SPES2, S2ES2, SPTrain.
workplace performance. Tasks within database management and statistical processing could be responsible for higher self-ecacious performance. Moreover, employees having database management, statistical processing, programming, and educational software use required on the job testify to the need for training and education parallels to job performance. Not only is it imperative that technical education compliment job requirements but consideration should be given to providing employees with the opportunity to apply such technical skills with the possibility of attaining higher pro®ciency. The use or challenge of these technical skills, although untrained, may produce motivated cognition to perform. Additionally, frequency of computer use is in¯uential to higher performance in computer technology. Employees reporting consistent use of computers maintain
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more steady and productive performance that those who use computers on a more sporadic basis. Home use, too, provides individuals with more con®dence to perform. Using computers at home allows more time for use and opportunity to improve one's skill level. Providing employees with more opportunities to expand their skill levels and raising the technological requirements in the workplace is likely to induce this essential degree of use. Likewise, employees given training responsibilities with computer technology increase their own use and level of skill and, thus, are more productive. This increased desire to perform can be attributed to authority recognition as well as the simple opportunity to use more technology on the job. Being more self-ecacious in computer technology requires some combination of home use, previous computer training (database management), computer use on the job (database management, spreadsheets, programming, educational software), frequency of use, and training responsibilities. Individuals with the opportunity to consistently use computers at home and work and who have had previous training in applications similar to database management can apply those applications to the job. Moreover, they are more apt to increase those skills through more technology application on the job. Also, employees having programming required on the job have more occasions to enhance their abilities through home use, training responsibilities, and overall frequency of use. Those performing programming on the job are more con®dent in performance due to increased use in a variety of circumstances. While providing a short distance between the time of training and performance has long been considered a necessary ingredient to transfer, this study indicates it as insigni®cant. According to the results and utilizing a 2.5-year time span, time distance did not instill self-ecacious performance with technology. Self-ecacy of computer technologies did not ¯uctuate regardless of how long training was received. It appears that with training as a prerequisite or to some existence, employees continue to be self-ecacious for a 2.5-year period. Therefore, the duration and usefulness of this training for technology self-ecacy provided in this study is 2.5 years. 10. Recommendations and implications This study re-emphasizes the necessitated role of organizations in equipping any and all human beings with technical workplace skills. Organizations that fail to provide employees with occasions to increase productivity will be negatively impacted by high turnover, more employee expenses, lower quality, and lost market share. Today, education and training programs must prove return to organizational worth. By monitoring the self-ecacy of such programs, educators can provide instruction which meets the challenges of employees and renders the productivity desired by organizations. Because time did not impact self-ecacy in computer technology, educators can assume that quality instruction remains self-ecacious and produces pro®cient performance for a 2.5-year period. However, prior to the this time frame, educators should be prepared to upgrade computer training and to implement a
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continuing computer education program to maintain con®dence and consistency in performance as technology changes. The research does recognize that there may be other in¯uences that contribute to technology self-ecacy and accentuates the need for research in those areas. For example, an individual's job may be indicative of how con®dent they are with the applications they use. Job components should be addressed to determine if responsibilities match with education. Performance in terms of self-ecacy should be assessed to determine what components of the job or education contribute to selfecacy. The research also recommends that educators search for the individual task within the training that would produce higher self-ecacy levels. As with database management and programming, there may be aspects of this training that can be introduced on other courses which will ensure technology self-ecacy. Curriculums, based upon con®dence-producing minute particles of technological interaction, can be prepared to enhance and extend self-ecacy and establish a employee comfortable con®dence zone for performance. Organizations, too, must be willing to extend the skill level of their employees through greater use and challenge. Organizations must make greater use of techniques such as delegation so that employees can be better challenged with use opportunities, thereby increasing their skills in present and unknown areas of technology. This can imply that other methods such as job rotation and placement in a variety of jobs produces higher self-ecacious performance. Giving training responsibilities is one proven method of increasing personal and co-worker performance. This study infers an even greater need for organizations to consider the impact of training and education on their overall performance. Since time distance was not a factor in producing higher levels of self-ecacy, organizations should seek to continuously improve their employee technical education programs. Without further study, it is possible to imply that our technical education programs remain selfecaciously stable or they never produced self-ecacious behavior at all.
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