The Role of Social Cognitive Career Theory in Information Technology based Academic Performance

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The Role of Social Cognitive Career Theory in
Information Technology based Academic

S h e i l a M . S m i t h
Positive academic efficacy beliefs elevate educational expectations that lead to academic
success (Bandura, 1997; Lent, Brown, & Hackett, 1994). The purpose of this study was to
explore the relationship of the variables: past performance, computer self-efficacy, outcome

expectations, academic grade goal, and academic performance within social cognitive career
theory’s model of performance (Lent, Brown, & Hackett, 1994). The study focused on the
effects of social cognitive variables on academic performance in an information technology

course. Participants were 193 undergraduate students (82 women and 111 men) who
completed a background questionnaire, the Information Technology Proficiency Exam, the

Computer Self-Efficacy scale, and the Technology Outcome Expectations scale. Based on path
model results, the findings suggest that students’ academic performance is related to past
performance. Consistent with theory, the findings suggest that academic performance is

influenced by computer self-efficacy via the establishment of an academic grade goal. In the
present study, the past performance variable failed to predict academic performance when
impacted by outcome expectations.


Personal efficacy plays a vital role in educational
future involvement with information technologies.
attainment. Intellectual growth is partially
Exposure to information technology is essential to
determined by individual belief in personal ability
academic achievement and career development.
to master various subjects and regulate self-learning
Career success in almost every occupation depends
(Schunk, 1989, 1994). Efficacy beliefs influence
on interaction with information technology.
academic motivation and aspirations, level of
A significant nexus of vocational education
interest in intellectual pursuits, scholastic
involves the construct of computer self-efficacy,
achievements, and academic goal persistence
defined as a judgment of one’s capability to use a
(Bandura, 1995, 1997; Schunk, 1994). Positive
computer (Compeau & Higgins, 1995). Computer
academic efficacy beliefs elevate educational
self-efficacy is an important personal trait that
expectations that lead to academic success
influences an individual’s decision to use computers
(Bandura, 1997). A strong sense of personal
(Compeau & Higgins, 1995). According to
efficacy creates self-directed lifetime learners who
Bandura’s (1986) social cognitive theory, self-
are valued and economically rewarded in today’s
efficacy is personal judgment of the ability to a pply
society (Lent, Hackett, & Brown, 1999). Strong
possessed skills (can I do this?). People are often
efficacy beliefs, along with fundamental learning
capable of achieving desired performance but do
tools supplied by formal education, result in
not exhibit behavior that will lead to task
students who possess skills necessary for social and
completion. Self-referent thought converts
economic stability.
knowledge into action (Bandura, 1986).
A lack of confidence with information
technology may hamper academic and career
Sheila M. Smith is Assistant Professor, Department of
success. Hill, Smith, and Mann (1987) found
Business Information Technology, College of Business,
confidence in ability to use a computer predictive of
Ball State University, Muncie, Indiana.
Information Technology, Learning, and Performance Journal, Vol. 20, No. 2, Fall 2002


Bandura’s (1986) social cognitive theory has
P u r p o s e o f S t u d y
been expanded to include academic performance
with the development of social cognitive career
The purpose of this study was to explore the
theory (SCCT) (Lent, Brown, & Hackett, 1994,
relationship of the variables: past performance,
1996). The word “career” used in the title of
computer self-efficacy, outcome expectations,
SCCT is inclusive of academic interest, choice, and
academic goal, and academic performance within
performance. SCCT was designed to provide a
social cognitive career theory’s model of
framework for explaining both academic and career
performance. An additional purpose of the study
behaviors. Social cognitive career theory views
was to explore the use of the path model of
academic progress as a developmental complement
performance as outlined in social cognitive career
to career interest and choice. SCCT emphasizes
theory (Lent, Brown, & Hackett, 1994). In
three social cognitive variables that may be relevant
particular, it focused on the effects of the social
to academic development: self-efficacy, outcome
cognitive variables on academic performance in an
expectations, and goals. According to Lent, Brown,
information technology course. In the present
& Hackett (1994), self-efficacy refers to “people’s
research, past performance, computer self-efficacy,
judgments of their capabilities to organize and
outcome expectations, and academic goal were
execute courses of action required to attain
predicted to relate positively to academic
designated types of performance” (p. 83). SCCT
performance. Specifically, the study analyzed the
defines outcome expectations as the desired
following hypotheses among undergraduate
consequences of a course of action and goals as the
effort required to engage in an activity. The

theoretical framework is based on three interlocking
HO1: Past performance would account for a
models: a) interest development, b) choice, and c)
significant amount of academic
performance (Lent, Brown, & Hackett, 1994).
performance variance.
Several recent path analyses have provided
HO2: Past performance would account for a
initial support for the three models’ ability to predict
significant amount of academic
the math and science academic interests, choices,
performance variance when influenced by
and performance of undergraduate students (Gainor
computer self-efficacy.
& Lent, 1998; Lopez, Lent, Brown, & Gore, 1997;
HO3: Past performance would account for a
Lapan, Shaughnessy, & Boggs, 1996). However,
significant amount of academic
the social cognitive career theory’s model of
performance variance when influenced by
performance has not been extended to the study of
computer self-efficacy and academic goal.
academic performance of undergraduate students

enrolled in an information technology course.

Figure 1: Social-Cognitive Career Theory Model of Performance

Academic Goal

Source: Adapted from R. W. Lent, S. D. Brown, and G. Hackett (1994) Social Cognitive Career Theory Model of Performance.

The Role of Social Cognitive Career Theory in Information Technology based Academic Performance 3
HO4: Past performance would account for a
within the performance model concerns
significant amount of academic
“performance goals,” which refer to the level of
performance variance when influenced by
attainment toward which one aspires within chosen
outcome expectations and academic goal.
performance domains.
HO5: Past performance would account for a

significant amount of academic
Research Studies
performance variance when influenced by

computer self-efficacy, outcome
Comparing self-efficacy beliefs and academic
expectations, and academic goal.
outcomes among students considering science and

engineering careers was initially explored by Lent,
R e v i e w o f L i t e r a t u r e
Brown, and Larkin (1984). Their study revealed

that both level (degree of difficulty) and strength
Social Cognitive Career Theory
(endurance) of self-efficacy could predict academic

performance as well as persistence for
Social cognitive career theory (SCCT) (Lent, Brown,
undergraduates enrolled in a career/educational-
& Hackett, 1994, 1996) is concerned with two
planning course and considering science or
primary aspects of academic performance: the level
engineering as an academic major. High self-
of achieved success or proficiency and the degree of
efficacy beliefs correspond with academic achievers
persistence despite encountering obstacles. SCCT
who received higher grades and persisted longer in
assumes that complicated task performance requires
technical/science programs. Lent, Brown, and
possession of requisite mastery skills and works in
Larkin (1986) extended the findings of their 1984
tangent with a sense of personal efficacy that
study by combining self-efficacy with past
enables the translation of skills into productive
performance, achievement, and vocational interest
performances. SCCT proposes that the self-efficacy
to determine academic outcomes, persistence, and
belief system is impacted by past performance (as
range of perceived technical/science career choices.
reflected by achievement or ability indicators)
The major findings of the 1986 study supported
therefore directly or indirectly affecting
and extended previous results showing that
performance. Past performance, self-efficacy,
academic outcomes, vocational interest, and
outcome expectations, and goals combine to
perceived range of technical/science career options
influence performance.
are related to self-efficacy expectations.
Performance Model. The model of
Hackett, Betz, Casas, and Rocha-Singh (1992)
performance, one of the three interlocking models
consistently found academic self-efficacy the
within social cognitive career theory is often seen as
strongest predictor of academic performance in
useful in explaining achievement relative to goals
college-level engineering students. In addition to
that are either personally selected (where activities
academic performance, interest in engineering
are mandated by external agents) or personally
occupations, positive outcome expectations, and
adopted (Lent et al., 1994). The performance
faculty encouragement were positively related to
model is concerned with the level (or quality) of
academic self-efficacy. Stress and faculty
people’s accomplishments, as well as with the
discouragement were negatively related to academic
persistence of their behavior in career-related
self-efficacy. Although few gender differences were
pursuits (Lent et al., 1996).
noted, women reported a significantly lower level of
According to Lent et al. (1994, 1996), there is
positive outcome expectations. Ethnicity was a
a connection among past performance, self-efficacy,
significant predictor of academic self-efficacy, but
outcome expectations, and goals in determining
was not predictive of performance.
performance outcomes. Additionally, consistent
Lapan, Shaughnessy, and Boggs (1996)
with social cognitive career theory’s triadic-
conducted a longitudinal study assessing the
reciprocal view of interaction, they propose a
influence of self-efficacy beliefs on choice of
feedback loop between performance attainments
math/science majors before college entry.
and subsequent behavior. The nature of goals
Following high school completion, subjects


completed self-efficacy measures and interest
efficacy was predictive of students’ performance on
measures primarily related to mathematics and
web-based instruction search exams, but not written
science. To complete the longitudinal study,
approximately 3.5 years after the initial

measurement administration, 101 of the original
M e t h o d
148 subjects’ university records were examined to

determine selected college major. Significant
gender difference was found at both examination

periods. Men reported greater interest in math and
Participants were 193 undergraduate students at a
science as entering college freshmen and as college
large Midwest university. Students were enrolled in
juniors. Path analysis revealed a direct link
three sections of an introductory information
between gender and math self-efficacy beliefs.
technology course entitled Business Information
Lopez, Lent, Brown, and Gore (1997)
Systems taught by the same instructor. Although six
implemented a path analysis model to test the four
different academic departments (architecture,
principal sources of self-efficacy information
communications, fine arts, science and humanities,
(mastery experiences, vicarious learning, social
teachers college, and honors) were represented, the
persuasion, and affective states) as outlined by
students were primarily from the college of business
Bandura (1986). Past performance (mastery
(n = 129, 67%). The 193 students (82 women
experiences) had the largest impact on self-efficacy
and 111 men) ranged in age from 18 years to 41
beliefs followed by social persuasion. In contract to
years old with a mean age of 19.23 (SD = 2.52).
other studies, gender differences were not found in
Grade level classification was: freshman (n = 36,
math-related academic interest or performance of
18.7%), sophomore (n = 116, 60.1%), junior (n =
the high school students examined in the study.
28, 14.5%), and senior (n = 13, 6.7%). The
Women reported receiving more social persuasion
ethnic composition of the students was: white (n =
and vicarious learning than men. Women also
158, 81.9%), African-American (n = 21,10.9%),
earned higher math course grades than men
Hispanic (n = 5, 2.6%), Asian (n = 2, 1%), and
students in this study.
other (n = 7, 3.6%).
Jinks and Morgan (1999) found moderate, yet

positive correlation between self-efficacy beliefs and
academic performance among secondary school

students. Using three samples of students from
Upon receiving permission from the internal review
urban, suburban, and rural school districts, Jinks &
board, research measures were administered during
Morgan found academic self-efficacy related with
a sixteen-week introductory information technology
students’ self-reported grades in four core subjects
course. An information technology proficiency
of math, social studies, science, and reading.
exam, computer self-efficacy scale, outcome
Student who expressed high self-efficacy beliefs also
expectations scale, and a background questionnaire
reported higher grades.
were administered during the first week of the
Ju Joo, Bong, & Chai (2000) examined the
course prior to any instruction. Asking students
relationship among self-efficacy for self-regulated
what grade they expected to receive at the end of
learning, academic self-efficacy, Internet self-
the course assessed the academic goal variable.
efficacy, and strategy use to determine the
The academic performance variable was derived
applicability of self-efficacy theory in a web-based
from the students’ end-of-term course grade. The
instruction environment. Path analysis revealed
course grade was based on completion of the
students’ self-efficacy for self-regulated learning
information technology course that was composed of
positively related to confidence both in the typical
course exams (42%), computer lab assignments
classroom learning and in using the Internet.
(38%), and a final information technology
Students’ previous experiences working with
presentation (20%). The letter grade distribution
computers significantly related to self-efficacy
was A (n = 27, 14%), B (n = 76, 39.4%), C (n =
perceptions toward using the Internet. Internet self-
74, 38.3%), D (n =11, 5.7%) and F (n = 5, 2.6).

The Role of Social Cognitive Career Theory in Information Technology based Academic Performance 5
Using assigned research identification numbers
Mathematics scale developed by Fennema and
ensured confidentiality.
Sherman (1976). The scale assessed students’

perceptions of the importance of information
technology to their future academic and career

plans. Positively and negatively worded items (e.g.,
Information Technology Proficiency Exam. To
“Taking information technology courses will help
assess past performance, a 63-item Information
me make better career decisions”) were rated on a
Technology Proficiency Exam (ITPE) designed
5-point Likert scale ranging from strongly agree (5)
specifically for research purposes was developed by
to strongly disagree (1). Negatively worded items
the investigator. This scale was designed to assess
were reversed scored so that the higher scores
students’ knowledge of fundamental information
indicated stronger beliefs. A Cronbach alpha of .87
technology concepts. Questions based on
was derived for this sample. Confirmatory factor
introductory information technology subjects were
analysis produced factor loadings of .25 on one
generated from 10 chapters in the textbook used in
item and .35 or above on the other 12 items on the
the information technology course. The chapters
were Introduction to Computers (6 items),
Background Questionnaire. The background
Application Software (4 items), The System Unit (5
questionnaire included measures of demographic
items), Input (5 items), Output (5 items), Storage (6
characteristics (gender, age, ethnicity, academic
items), The Internet (5 items), Systems Software (6
department, and grade classification).
items), Communications and Networks (5 items),

and Database Management (4 items). The scale
Data Analysis
was composed of multiple-choice questions with

only one possible correct answer. Each item was
Hypothesized relationships among past
worth 1 point resulting in a scale value of 63 points.
performance, computer self-efficacy, outcome
Although this scale has not been used in previous
expectations, academic goal, and academic
research, the researcher administered, revised, and
performance were tested with correlations. The
examined the psychometric properties for three
Pearson product-moment correlation coefficient was
consecutive years prior to this study. Confirmatory
the statistical measure used to determine the
factor analysis produced factor loadings of .30 or
strength of the theoretical variables. An alpha level
above on all the items on the ITPE.
of 0.05 with a two-tailed probability was used to
Computer Self-Efficacy Scale. To assess
determine significance.
computer self-efficacy this study used Torkzadeh
To assess the adequacy of the academic
and Koufteros’ (1994) Computer Self-Efficacy scale
performance model, a structural equation model
(CSE). The 30-item CSE scale measures self-
was conducted using Amos 3.6 (Arbuckle, 1997), a
perception of computer-related skills and
statistical software application. Structural equation
knowledge. Each item proceeded by the statement,
modeling (SEM) is a confirmatory technique
“I feel confident” was rated on a 5-point Likert-type
generally used to test a theory (Tabachnick &
response format (1 = strongly disagree and 5 =
Fidell, 1996). SEM allows for the simultaneous
strongly agree). High scores indicated a high
examination of multiple relationships. Complex
degree of confidence in one’s ability to use
relationships are analyzed in a single model (Hair,
computers. A high Cronbach alpha of .92 was
Anderson, Tatham, & Black, 1998).
derived for this sample. Confirmatory factor
Four indexes to assess the overall fit of the
analysis produced factor loadings of .60 or above
hypothesized model to observed data were used.
on all the items on the CSE.
The most fundamental measure of overall fit is the
Technology Outcome Expectations Scale.
likelihood-ratio chi-square statistic (Jõreskog &
Outcome expectations were measured with the
Sõrbom, 1984), a nonsignificant chi-square suggests
Technology Outcome Expectations scale (TOE), a
model adequacy. The chi-square index is sensitive
13-item measurement modified by the investigator.
to sample size and violations of the assumption of
The instrument was based on the Usefulness of
multivariate normality; therefore, alternative fit


indexes are generally used to complement the chi-
the proposed model fits the observed covariances
square measure (Tabachnick & Fidell, 1996). The
and correlations (Hair, Anderson Tatham, & Black,
goodness-of-fit index (GFI) (Bentler, 1980), the
1998). The GFI (.99), AGFI (.96), and the CFI =
adjusted goodness-of-fit index (AGFI) (Bentler,
.99 indicated that the model fit the data adequately
1983), and the Comparative Fit Index (CFI)
(Loehlin, 1992; Tabachnick & Fidell, 1996; Hair,
(Bentler, 1990) were the alternative indexes used in
Anderson, Tatham, & Black, 1998).
this study. A value of .90 or greater is commonly
Hypothesis one stated that past performance
recommended for an acceptable level of fit (Hair,
determined by assessed knowledge of information
Anderson, Tatham, & Black, 1998).
technology concepts would predict the academic

performance measured by the final grade obtained
R e s u l t s
in an introductory course. Consistent with theory

and prior results (Lopez et al., 1997), past
Descriptive data were calculated for all the social
performance produced a significant path to
cognitive theoretical variables. Table 1 presents the
academic performance (? = -.19) at the .05 level,
minimum, maximum, mean, and standard deviation
providing support for hypothesis one. Hypothesis
for all variables. The past performance variable
two states that past performance, influenced by
mean indicated that participants possessed limited
computer self-efficacy, would predict academic
prior knowledge about basic information technology
performance. Past performance accounted for 15%
concepts (M = 40.77, SD = 6.52). Based on a
of the computer self-efficacy variance (R2 = .15)
63-point total value on the Information Technology
and produced a significant path to computer self-
Proficiency Exam, the participants’ mean score was
efficacy (? = .40). However, the path from
only 65% of the exam total. Using a grading system
computer self-efficacy to academic performance (?
in which a 4.0 represented the letter grade A, the
= -.20) was not found in the present study. The
academic performance variable mean of 2.93
results support hypothesis two only partially.
equated to an average course grade of slightly less
Hypothesis three suggested that past
than a B.
performance, when influenced by both computer
Table 2 shows results for the intercorrelations
self-efficacy and academic goal, would relate to
of social cognitive theoretical variables. Past
academic performance. Past performance (? =
performance had a statistically significant positive
.40) when influenced by computer self-efficacy (?
correlation with computer self-efficacy (r = .39)
= .27) and academic goal (? = .39) positively
and academic goal (r = .22), at the .01 level. A
statistically significant correlation between
computer self-efficacy and academic performance
Table 1: Descriptive Statistics (N=193)
did not exist (r = -.03). At the .01 level, self-

efficacy had a statistically significant positive
Past Performance
correlation with outcome expectations (r = .20)
Computer self-efficacy
and academic goal (r = .30). There was a
Outcome Expectations
statistically significant positive correlation between
Academic Goal
academic goal and academic performance (r =
Academic Performance
.34). Contrary to theory, outcomes did not have a
relationship with the academic goal or academic
Table 2: Correlations Among Theoretical Variables
performance variable.

A path model using the maximum-likelihood
estimation method tests the hypotheses among the Past Performance
.39** .11
.22** .14*
theoretical variables. Figure 2 presents results
Computer self-efficacy

.20** .30** -.03
Outcome Expectations

from the path analysis. Specifically, the ?2 value
Academic Goal

was not significant (?2 = 5.10, df = 4, p =.28).
Academic Performance

A non-significant chi-square value indicates that
*p < .05 **p < .01

The Role of Social Cognitive Career Theory in Information Technology based Academic Performance 7
related to academic performance at the .01 level.
D i s c u s s i o n
As stated in hypothesis three, past performance,

computer self-efficacy, and academic goal combined
The theoretical foundation of social cognitive career
to explain 13% of the variance in academic
theory’s performance model outlines the
performance (R2 = .13).
motivational factors that can influence students’
Hypothesis four stated that past performance,
performance. This study was an initial attempt to
influenced by both outcome expectations and
extend the application of social cognitive career
academic goal, would predict academic
theory to an information technology-mediated
performance. The path from past performance to
learning environment. The findings are generally
outcomes (? = .04) was not significant; past
consistent with social cognitive career theory
performance accounted for only 4% of the outcome
expectations variance (R2 = .04). Although the
The path analysis produced good support for
academic goal variable produced a significant path
the model in which past performance helped
to academic performance (? = .39), the path from
determine academic performance. Past
outcome expectations to academic goal (? = -.01)
performance, as measured by the score on the
was not significant; therefore, hypothesis four was
proficiency exam taken at the beginning of the
not supported.
course contributed significantly to academic
According to hypothesis five, the combination
performance defined by the final course grade.
of past performance, computer self-efficacy,
Introduction and repeated exposure to basic
outcome expectations, and academic goal would
information technology concepts advances
predict academic performance. The path from past
performance levels. Activity engagement greatly
performance to computer self-efficacy (? = .40)
contributes to successful performance (Bandura,
and the path from computer self-efficacy to outcome
1995, 1997; Lent et al., 1994, 1996).
expectations (? = .20) was significant at the .01
Administration of a pre-course proficiency exam
level. Although the outcome expectations variable
may help educators identify high and low
did not provide a significant path to academic goal
competency students. Instructors can use
(? = -.01), academic goal (? = .39) produced a
knowledge of students’ previous experience with
significant path to academic performance at the .01
information technology to design a flexible
curriculum. They can categorize computer-
Figure 2: A Path Analysis of the Social-Cognitive Academic Performance Model. Standardized coefficients and their
standard errors (in parentheses). *p<.05; **p<.01;
?2=5.10; df=4; p=.28; Goodness-of-fit index=.00;
adjusted goodness-of-fit index=.96; comparative fit index=.99.

.19 (.03)
.40 (.01)
- 20 (.10)
.27 (.05)
20 (.04)
39 (13)
.04 (.01)
-.01 (.01)
18 (.53)

Source: Adapted from R. W. Lent, S. D. Brown, and G. Hackett (1994) Social Cognitive Career Theory Model of Performance.


mediated tasks into difficulty levels and assign them
exceptionally important in governing performance
to students based on the success of the students’
attainment. Based on previous experience with
past performance. Instructors can use competency
information technology, students often enroll in
assessment to establish classroom policies and
introductory information technology courses with
procedures that will minimize the knowledge gap.
the impression that computer-mediated assignments
The path from past performance to self-efficacy
are easily accomplished. They, therefore, expend
was significant; however, the direct path from
only a minimal effort to achieve excellence. Goal
computer self-efficacy to academic performance was
setting may help students pursue excellence and
insignificant. An examination of the computer self-
concentrate on attaining quality performance levels
efficacy and academic performance variable mean
(Schunk, 1989). Although goals do not ensure
scores (see Table 1) indicates that participants were
effective performance, they can help regulate one’s
over-confidence about their ability to perform in an
performance behavior (Lent et al., 1994).
information technology course. Often students
The model offered adequate fit to the data;
arrive in introductory information technology
however, outcome expectations did not produce a
courses with inaccurate perceptions about their
significant path to academic goal that subsequently
knowledge and abilities. Perceived competencies
affected academic performance as predicted by the
that over- or underestimate assessed performance
model. Since the outcome expectation variable did
may reveal a lack of self-knowledge. The inability
not produce a significant path as outlined by social
to assess computer self-efficacy beliefs accurately
cognitive career theory, this warrants further
frequently creates an academic performance
refinement of the technology outcome expectation
disparity (Bandura, 1997). Self-assessment
measurement. In addition to measurement
measures throughout the curriculum can help
revisions, future administration of the technology
students accurately measure their level of
outcome expectations scale may require a thorough
confidence and competence. Efficacy appraisals
explanation that provides clarification of the
will help students develop accurate self-knowledge
variable. Although outcome expectations failed to
regarding their capabilities.
provide a significant path to academic goal, the
Consistent with theory, the findings suggest that
overall findings suggest that past performance,
past performances and computer self-efficacy
computer self-efficacy, and outcome expectations
(confidence) influences academic performance via
are related and that academic goal influences
the establishment of an academic goal. According
academic performance.
to Lent, Brown, and Hackett (1994) goals may be
Further exploration of the model has practical
the determining factor in activity engagement and
implications for practice. Instructional practices
performance accomplishments. In addition, social
that examine the impact of the social cognitive
cognitive career theory posits that the quality of
career theory variables in a technology-based
performance attained may partly depend on the
learning environment may lead to valuable
objective of one’s academic goal. Goal setting may
information that helps explains academic behaviors.
contribute to improved performance if they are
Courses designed to develop and advance
realistic and accompanied by appropriate behavior
information technology concepts may benefit from
(Lent, Brown, & Hackett, 1994; 1996).
assessing students’ previous experiences with
Encouraging students to establish an initial grade
technology and efficacy percepts. Instructors
goal and providing feedback about the self-
should share assessment results with students to
assessment measure may heighten their level of
heighten awareness and help remedy academic
academic performance. Instructors should
disparities. Awareness may help student formulate
periodically revisit academic goals throughout the
a realistic appraisal of efficacy perceptions and
course, and modify them according to progress.
ability. In conjunction with past performance and
Evaluation of the path model reveals that
computer self-efficacy assessment, students should
academic performance when influenced by
be encouraged to set an academic grade goal,
academic grade goal was significant. According to
understand the consequences of their academic
social cognitive career theory, goals are

The Role of Social Cognitive Career Theory in Information Technology based Academic Performance 9
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Hackett, G., Betz, N. E., Casas, J. M., & Rocha-Singh I.
academic success.
A., (1992). Gender, ethnicity, and social cognitive
Using social cognitive career theory to
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Hill, T., Smith, N. D., & Mann, M. F. (1987). Role of
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efficacy expectations in predicting the decision to
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use advanced technologies: The case of computers.

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