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Technical education transfer: perceptions ofemployee computer technology self-e�cacy

C.A. Decker*Lincoln Memorial University, 2227 Highway 11 North, Niota, TN 37826, USA

Abstract

This study investigated in¯uences on employee self-e�cacy of computer technologiesresulting from computer training programs that were intended to meet individual and organ-

ization objectives for university personnel. Subsequent to training, an assessment of employeecomputer technology self-e�cacy was necessary for determining self-e�cacious duration andthe usefulness of technical education. A descriptive survey design was used to gather data

from a population of 2597 university employees. Results indicated employee computer tech-nology self-e�cacy levels remained stable for a 2.5-year period. In addition, select subscales ofthe variables previous classroom computer training and computer use required on the job

predicted computer technology self-e�cacy. Frequency of computer use, home computer use,and training responsibility were also noted to in¯uence the transfer of training process as itpertains to computer technology self-e�cacy. Interaction relationships were also discoveredamong certain disciplines of computer use and degree of computer use. Implications of the

study are relevant to employee placement and workplace computer education needs. # 1999Elsevier Science Ltd. All rights reserved.

Keywords: Computer technology; Self-e�cacy; Training and education transfer

1. Introduction

The performances of organizations, according to Heilman and Hornstein (1982),are a�ected by human forces rather than technical capabilities. For nearly a century,organizations have searched for the means to best achieve their organizationalobjectives. This quandry is now coupled with the use of progressive computer tech-nology that requires consistent yet scarce human resource pro®ciency for positiveorganizational results. Historically, decision makers have overlooked the impact of

Computers in Human Behavior 15 (1999) 161±172

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employee self-con®dence or self-e�cacy in performance as a component in achievingorganizational success. Devastating demands and requirements of computer tech-nology are likely to erode one's con®dence to perform, as well as any desire toundertake any computer-related activities. Employee self-e�cacy perceptions oftechnological advancements are re¯ected in the performance and pro®ciency real-ized by the organization. Workplace performance and the employee's willingness tolearn computer technologies and their related tasks is hindered by low self-e�cacylevels (Bandura, 1977; Gist, 1987). Consequently, attention to providing technicalworkforce preparation that transfers or results in self-e�cacious computer technol-ogy interaction is a necessity.

2. The foundation of self-e�cacy

A representative de®nition of self-e�cacy is an individual's belief in their abilityto perform a particular task (Bandura, 1977, 1982; Gist, 1989). Beginning some 20years ago, researchers posited that performance was a result of human interactions,mental cognition, and perceived self-e�cacy. Although this framework producedperformance, the degree of activity varied from pro®cient to reactive. As a result, thelevel of self-e�cacy one achieves through human interactions and mental cognitionis the focus believed to generate positive and skilled transfer to work environments.Bandura (1977) stated that information needed to make self-e�cacious judgmentsare obtained by: (1) performance accomplishments or enactive mastery; (2) vicariouslearning experiences; (3) verbal persuasion; and (4) physiological arousal. Perfor-mance accomplishments make a cognitive register in which self-thought comparesthe possibilities of future task attainment. Bandura (1982) said:

People register notable increases in self-e�cacy when their experiences dis-con®rm misbeliefs about what they fear and when they gain new skills to man-age threatening activities. If in the course of completing a task, they discoversomething that appears intimidating about the undertaking, or suggests limita-tions to their mode of coping, they register decline in self-e�caciousness despitetheir successful performance. (pp. 125±126)

In the same study, he referred to verbal persuasion as the use of conversation andcollaboration to reach a level of self-e�cacy. Physiological arousal, conversely, actsas the responsible agent for changing emotions to ®t a self-e�cacy judgment mode.Vicarious learning experiences occur by watching and absorbing the struggles andsuccesses of others (Popper & Lipschiz, 1993).Gist (1989) and Gist and Mitchell (1992) noted self-e�cacy as the basis for

choosing what to do, sustaining amount of e�ort needed for attainment, and pre-serving experiences. Bandura (1977) found self-e�cacy judgments to result in fourtypes of behavior: (1) performance; (2) coping; (3) arousal; and (4) persistence ofsituations individuals choose for themselves. Bandura (1982) noted self-e�cacy asa determinant of choice behavior because it in¯uences the choice of behavioral

162 C.A. Decker / Computers in Human Behavior 15 (1999) 161±172

settings. When individuals recognize coping as inadequate for addressing threaten-ing situations, those situations are avoided. On the other hand, if people feel copingis acceptable, they may participate in that kind of situation. Bandura (1988) wrotethat three elements: (1) skills matched with basic competencies; (2) complex skillsbroken down into subskills; and (3) learning to apply skill competencies and sub-skills with di�erent people and circumstances, result in positive performance. Heemphasized that new skills are rarely used for long unless they are applied in worksituations which provide an environment of experience for building personal con-®dence. Accordingly he said self-e�cacy:

. . . in dealing with one's environment is not a ®xed act or simply a matter ofknowing what to do. Rather, self-e�cacy involves a generative capability inwhich component, cognitive, social, and behavioral skills must be organizedinto integrated courses of action to serve innumerable purposes . . . Self-e�cacyjudgments, whether accurate or faulty, in¯uence choice of activities and envi-ronmental settings. (Bandura, 1988, pp. 122±123)

Bandura (1977, 1982) contended that training programs can in¯uence sources ofinformation that, in turn, result in self-e�cacy judgments to reliably measure per-formance in program objectives.Self-e�cacy theory is applicable in numerous settings which require performance

or where performance is expected. In this study, self-e�cacy is considered within thecontext of computer technologies. Murphy, Coover, and Owen (1988) de®ned com-puter self-e�cacy as an individual's belief that they can perform a speci®c computertask. Narrowing the de®nition of self-e�cacy, Kinzie and Delcourt (1991) recog-nized ¯uctuating levels of self-e�cacy with regard to speci®c technologies andderived the term self-e�cacy of computer technologies. They de®ned e�cacy ofcomputer technologies as an individual's belief in their ability to use a speci®c com-puter technology such as word processing, e-mail, and CD-ROM databases.

3. Review of literature

Self-e�cacy in computer technology transfer research is quite incomplete withregard to the combination of employees, work environments, and their computer use/technology impact on organizational e�ectiveness. Existing studies utilize service andsales organization employees such as nurses, clerical, administrative or professional,and sales representatives to determine factors that explain self-e�cacy (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 inaddressing factors impacting computer use self-e�cacy. Utilizing students andindustry employees, some studies recognized computer self-e�cacy as a component ofuser 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 di�erence in self-e�cacy and perceived

C.A. Decker / Computers in Human Behavior 15 (1999) 161±172 163

ease of use. E-mail self-e�cacy was also the focus of a study using university faculty(Kandies, 1995). Several studies used a variety of university employees and stategovernment employees to analyze how aspects of work a�ected general self-e�cacy(Jex & Gudanowski, 1992; Kennedy, 1993; Latham & Frayne, 1989; Prieto &Altmaier, 1994). Computer self-e�cacy and self-e�cacy of computer technologystudies 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 onself-e�cacy of computer technologies subsequent to training and self-e�cacy dura-tion 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 computertechnology skills from the classroom to the work environment continues to be a vitalfocus of organizational productivity. The present study provides educators andindustry with information for improving, monitoring, and evaluating their pro-grams. This study will speci®cally suggest technical education instructional tech-niques required in classroom and performance-based training to ensure positiveperformance at school and transfer to the workplace. Moreover, the study providesinformation regarding how individuals should be placed in levels of occupations inorder to maximize performance. Last of all, previous studies declare that theamount of time between training and application is an indicator of the degree towhich training transfers to on-the-job performance. While the variable time isexamined as a performance in¯uence in this study, it is also used to account for theduration of computer technology self-e�cacy. The lasting e�ect or usefulness oftrained technology self-e�cacy can be an indicator for educators and organizationsto improve curriculums, educational initiatives, or provide continuing education foremployees.

6. Research objectives

Speci®c research objectives addressed by this study were:

1. To determine di�erences in self-e�cacy of computer technology among uni-versity employees with regard to time (between training and survey time), homecomputer use, previous computer classroom training, computer use requiredon the job, training responsibilities, and frequency of computer use; and


2. To determine interactions in self-e�cacy of computer technologies amonguniversity employees with regard to time, home use, previous computer class-room training, computer use required on the job, training responsibilities, andfrequency of computer use.

7. Procedures and methodology

A descriptive ex post facto survey design was used to gather data from a popula-tion of 2597 university clerical/administrative, technical/maintenance, and facultyemployees who had received computer-related training in a 2.5-year period.The employees received this non-mandatory training ranging from word processingto programming in a 4-week period and for one time during the 2.5-year period. Atotal of 357 responses from a sample of 448 provided an overall response rate of80%. Dillman (1978) indicated that response rate is determined by dividing thenumber of questionnaires returned by the number in the sample minus those ineli-gible. Although the National Education Association Research Division (1960) bul-letin indicated a required sample size of 335 to ensure a 95% con®dence level, theresearcher felt it prudent for population generalization to have a random sample sizeof 500. Of the sample size, 52 questionnaires were associated with trainees who wereineligible which resulted in 448 viable assessments. Non-respondents were sent follow-up letters 1 and 2 weeks after the ®rst mailing culminating with a third follow-upattempt to encourage participation or to gain completions by phone. The `computerself-con®dence assessment' consisted of a background segment for gathering demo-graphic information, and the self-e�cacy of computer technologies scale (SCT).Kinzie and Delcourt (1991) developed the SCT scale in response to a lack of

instruments available for measuring self-e�cacy of computer technologies such asword processing, e-mail, and CD-ROM databases. The scale contained a total of 27activity items with 10 subscale items related to word processing, 10 items related toe-mail, and seven items related to compact disc (CD-ROM) databases. The scale wasvalidated by a pilot study utilizing a panel of 17 experts consisting of computer tech-nology instructors, measurement experts, educational consultants, and graduate stu-dents. A critique of the questionnaire was also provided by a university instrumentreview committee. After the pilot study, a Cronbach alpha of 0.84 was determined.Kinzie and Delcourt ®rst administered the questionnaire to 313 undergraduate andgraduate respondents across the USA at major universities. Items such as previouscomputer use and previous computer training were used for demographic informa-tion about the respondents. Study participants rated their degree of con®dence bystrongly disagreeing or strongly agreeing with the con®dence statements on a4-point Likert-type scale (1=strongly disagree, 4=strongly agree). Robust valida-tion procedures and previous applicability for studying a broadly skilled populationregarding general computer technology self-e�cacy resulted in the deemedappropriateness of the questionnaire for this workplace-based study. Similar toKinzie and Delcourt, this study also considered other factors such as previouscomputer use and previous computer training as contributors to an individual's


general computer technology self-e�cacy in order to posit additional types of com-puter 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 man-agement, e.g. Dbase, AskSam; word processing, e.g. WordPerfect; statistical proces-sing, 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 onthe job (database management, e.g. Dbase, AskSam; word processing, e.g. Word-Perfect; statistical processing, e.g. SPSSX, SAS; spreadsheets, e.g. Lotus, Excel; pro-gramming, e.g. Basic, Cobol; educational software, e.g. Teaching Typing), homecomputer use, frequency of computer use, and training responsibilities. The examplesprovided for the variables of previous computer training, and computer use requiredon the job were considered recognizeable to respondents and associative with appli-cations in use by the organization. The independent variable of time was provided bythe Institution.After the computer self-con®dence assessments were collected and compiled, data

analyses were performed using the UNIX mainframe computer system. The speci®cstatistical analysis was performed using the SAS1 (SAS Institute, 1985) statisticalsoftware residing on the UNIX system.Multiple analysis of variance (MANOVA) was used to best determine signi®cant

di�erences ( p<0.05) of time, home use, previous computer classroom training,computer use required on the job, training responsibilities, and frequency of com-puter use, on self-e�cacy of computer technologies. The Wilks' lambda multiplecomparison follow-up procedure was applied on each test of the independent vari-ables on the dependent variable to determine if signi®cant mean di�erences occur-red. For signi®cant di�erences found by the Wilks' lambda, the Tukey honestlysigni®cant di�erence (HSD) post hoc test was used to make all pairwise comparisonsamong the independent variable subscales. The dependent variable was the SCTscores while speci®c independent variables considered were time (between trainingand survey time; 0±6 months, 6 months±1 year, 1±1.5 years, 1.5±2 years, 2±2.5years), home use (yes or no), previous classroom computer training (database man-agement, word processing, statistical processing, spreadsheets, programming, edu-cational software), computer use required on the job (database management, wordprocessing, statistical processing, spreadsheets, programming, educational software),frequency of computer use (always, frequently, seldom, never), and trainingresponsibilities (yes or no), respectively.

8. Results

In Table 1, the MANOVA disclosed signi®cant di�erences in self-e�cacy ofcomputer 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 process-ing, programming, educational software), frequency of computer use, and training


responsibilities. There was no signi®cant di�erence in SCT scores among universityemployees with respect to time between training and survey time. The Wilks' lambdamultiple comparison follow-up procedures, as shown in Table 2, revealed signi®cantdi�erences in self-e�cacy of computer technologies in university employees withrespect to home computer use, previous classroom computer training (databasemanagement), computer use required on the job (database management, spread-sheets, programming, educational software), frequency of computer use, and train-ing responsibilities. Tukey's HSD all pairwise procedure, as shown in Table 3,revealed Tukey HSD post hoc signi®cant di�erences in SCT mean scores amongthose participants using computers always and those using computers frequently,never, and seldom. Other signi®cant di�erences were noted in SCT mean scoresbetween those using computers frequently and those using computers seldomly andnever. Signi®cant interactions, as shown in Table 4, were among programmingrequired on the job and frequency of computer use, home computer use, and train-ing responsibilities.

9. Conclusions and discussion

Results signify in¯uences to be considered when designing technical education ortraining for transferable performance to the workplace. Previous classroom computer

Table 1

Multivariate analysis of variance for self-e�cacy of computer technologies (SCT) scores of respondents

classi®ed by independent variables

df SS F Pr>F

SCT

Time 4 401.670 0.29 0.8829

Home computer use 1 5150.388 15.68 0.0001*

Previous classroom computer training

Database management 1 5531.120 20.39 0.0001*

Word processing 1 315.771 1.16 0.2814

Statistical processing 1 1064.938 3.93 0.0484*

Spreadsheets 1 544.999 2.01 0.1573

Programming 1 626.512 2.31 0.1296

Educational software 1 403.057 1.49 0.2238

Computer use required on the job

Database management 1 1730.186 6.75 0.0098*

Word processing 1 27.069 0.11 0.7454

Statistical processing 1 1380.465 5.38 0.0210*

Spreadsheets 1 870.975 3.40 0.0662

Programming 1 2941.893 11.48 0.0008*

Educational software 1 1549.418 6.04 0.0145*

Frequency of use 3 28 909.943 36.94 0.0001*

Training 1 1987.332 5.88 0.0158*

* p<0.05.


training (database management, statistical processing), computer use required onthe job (database management, statistical processing, programming, educationalsoftware), frequency of computer use, home use, and training responsibilities aredenoted as forces used to explain an individual's level of self-e�cacy from previouseducation and training. Previous classroom computer training in database manage-ment and statistical processing can contribute to opulent transfer of technical

Table 2

Multivariate analysis of variance for mean scores of respondents classi®ed by independent variables

Wilks' lambda df F Pr>F

Time 0.9779 8, 686 0.9592 0.4669

Home use 0.9500 2, 344 9.0429 0.0001*

Previous classroom computer training

Database management 0.9326 2, 311 11.2333 0.0001*

Word processing 0.9943 2, 311 0.8977 0.4086

Statistical processing 0.9875 2, 311 1.9719 0.1409

Spreadsheets 0.9889 2, 311 1.7858 0.1899

Programming 0.9904 2, 311 1.5085 0.2229

Educational software 0.9943 2, 311 0.8871 0.4129

Computer use required on the job

Database management 0.9377 2, 315 10.4716 0.0001*

Word processing 0.9990 2, 315 0.1556 0.8559

Statistical processing 0.9822 2, 315 2.8538 0.0591

Spreadsheets 0.9317 2, 315 11.5449 0.0001*

Programming 0.9647 2, 315 5.7572 0.0035*

Educational software 0.9798 2, 315 3.2515 0.0400*

Frequency of use 0.6791 6, 684 24.3380 0.0001*

Training 0.9689 2, 340 5.4529 0.0047*

* p<0.05.

Table 3

Tukey's honestly signi®cant di�erence (HSD)a procedure (simultaneous con®dence intervals) for

comparisons of subscale means for variable of frequency of computer use

Frequency of use subscales Lower con®dence limit Mean Upper con®dence limit

SCT b

A±F 6.174 10.914 15.654*

A±N 13.480 28.530 43.581*

A±S 24.088 34.123 44.158*

F±N 2.431 17.616 32.802*

F±S 12.973 23.209 33.444*

N±S ÿ11.981 5.592 23.165

SCT, self-e�cacy 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 di�erent.


workplace performance. Tasks within database management and statistical proces-sing could be responsible for higher self-e�cacious performance.Moreover, employees having database management, statistical processing, pro-

gramming, and educational software use required on the job testify to the need fortraining and education parallels to job performance. Not only is it imperative thattechnical education compliment job requirements but consideration should be givento providing employees with the opportunity to apply such technical skills with thepossibility of attaining higher pro®ciency. The use or challenge of these technicalskills, 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

Table 4

Multivariate analysis of variance for mean scores of respondents classi®ed by signi®cant main e�ect and

subscale interactions

Wilks' lambda df F Pr>F

DM�DM2 0.9997 2, 225 0.0307 0.9698

DM�S2 0.9996 2, 225 0.0449 0.9561

DM2�S2 0.9908 2, 225 1.0498 0.3517

DM�P2 0.9747 2, 225 2.9161 0.0562

DM2�P2 0.9990 2, 225 0.1110 0.8949

S2�P2 0.9943 2, 225 0.6460 0.5251

DM�ES2 0.9925 2, 225 0.8555 0.4264

DM2�ES2 0.9822 2, 225 2.0402 0.1324

P2�ES2 0.9904 2, 225 1.0916 0.3375

DM�Frequse 0.9973 2, 225 0.3092 0.7343

DM2�Frequse 0.9998 2, 225 0.0198 0.9804

S2�Frequse 0.9988 2, 225 0.1328 0.8757

P2�Frequse 0.9546 2, 225 5.3548 0.0053*

ES2�Frequse 0.9875 2, 225 1.4265 0.2423

DM�Home use 0.9837 2, 225 1.8661 0.1571

DM2�Home use 0.9907 2, 225 1.0608 0.3479

S2�Home use 0.9971 2, 225 0.3244 0.7233

P2�Home use 0.9639 2, 225 4.2165 0.0159*

ES2�Home use 0.9993 2, 225 0.0767 0.9262

Frequse�Home use 0.9876 2, 225 1.4099 0.2463

DM�Train 0.9902 2, 225 1.1173 0.3290

DM2�Train 0.9813 2, 225 2.1393 0.1201

S2�Train 0.9894 2, 225 1.2014 0.3027

P2�Train 0.9695 2, 225 3.5343 0.0308*

ES2�Train 0.9842 2, 225 1.8032 0.1671

Frequse�Train 0.9849 2, 225 1.7258 0.1804

Home use�Train 0.9979 2, 225 0.2354 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, pro-

gramming (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 DM�SP, SP�SP2,SP�P2, SP�ES2, S2�ES2, SP�Train.


more steady and productive performance that those who use computers on a moresporadic basis. Home use, too, provides individuals with more con®dence to per-form. Using computers at home allows more time for use and opportunity toimprove one's skill level. Providing employees with more opportunities to expandtheir skill levels and raising the technological requirements in the workplace is likelyto induce this essential degree of use. Likewise, employees given training respon-sibilities 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 authorityrecognition as well as the simple opportunity to use more technology on the job.Being more self-e�cacious in computer technology requires some combination of

home use, previous computer training (database management), computer use on thejob (database management, spreadsheets, programming, educational software), fre-quency of use, and training responsibilities. Individuals with the opportunity toconsistently use computers at home and work and who have had previous trainingin applications similar to database management can apply those applications to thejob. Moreover, they are more apt to increase those skills through more technologyapplication 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 fre-quency of use. Those performing programming on the job are more con®dent inperformance 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 asinsigni®cant. According to the results and utilizing a 2.5-year time span, time dis-tance did not instill self-e�cacious performance with technology. Self-e�cacy ofcomputer technologies did not ¯uctuate regardless of how long training wasreceived. It appears that with training as a prerequisite or to some existence,employees continue to be self-e�cacious for a 2.5-year period. Therefore, the dura-tion and usefulness of this training for technology self-e�cacy provided in this studyis 2.5 years.

10. Recommendations and implications

This study re-emphasizes the necessitated role of organizations in equipping anyand all human beings with technical workplace skills. Organizations that fail toprovide employees with occasions to increase productivity will be negatively impact-ed 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-e�cacy of such programs, educators can provide instructionwhich meets the challenges of employees and renders the productivity desired byorganizations. Because time did not impact self-e�cacy in computer technology,educators can assume that quality instruction remains self-e�cacious and producespro®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


continuing computer education program to maintain con®dence and consistency inperformance as technology changes.The research does recognize that there may be other in¯uences that contribute to

technology self-e�cacy and accentuates the need for research in those areas. Forexample, an individual's job may be indicative of how con®dent they are with theapplications they use. Job components should be addressed to determine if respon-sibilities match with education. Performance in terms of self-e�cacy should beassessed to determine what components of the job or education contribute to self-e�cacy.The research also recommends that educators search for the individual task within

the training that would produce higher self-e�cacy levels. As with database man-agement and programming, there may be aspects of this training that can be intro-duced on other courses which will ensure technology self-e�cacy. Curriculums,based upon con®dence-producing minute particles of technological interaction, canbe prepared to enhance and extend self-e�cacy and establish a employee comfort-able con®dence zone for performance. Organizations, too, must be willing to extendthe skill level of their employees through greater use and challenge. Organizationsmust make greater use of techniques such as delegation so that employees can bebetter challenged with use opportunities, thereby increasing their skills in presentand unknown areas of technology. This can imply that other methods such as jobrotation and placement in a variety of jobs produces higher self-e�cacious perfor-mance. Giving training responsibilities is one proven method of increasing personaland 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 afactor in producing higher levels of self-e�cacy, organizations should seek to con-tinuously improve their employee technical education programs. Without furtherstudy, it is possible to imply that our technical education programs remain self-e�caciously stable or they never produced self-e�cacious behavior at all.

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