Why College Undergraduates Intend to Pursue the Information Technology Major

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WHY COLLEGE UNDERGRADUATES INTEND TO PURSUE

THE INFORMATION TECHNOLOGY MAJOR:A MULTI-THEORETICAL PERSPECTIVE

by

Nathan D. Heinze

A Dissertation Submitted to the Faculty of

The Barry Kaye College o f Business

in Partial Fulfillment of the Requirement for the Degree of

Doctor of Philosophy

Florida Atlantic University

Boca Raton, Florida

December 2007

roduced with permission of the copyright owner. Further reproduction prohibited without permission.



UMI Number: 3288657

Copyright 2007 by

Heinze, Nathan D.

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Copyright by Nathan D. Heinze 2007

ii




WHY COLLEGE UNDERGRADUATES CHOOSE TO PURSUE THE

INFORMATION TECHNOLOGY MAJOR:

A M ULTI-THEORETICAL PERSPECTIVE

by

Nathan D. Heinze

This dissertation was prepared under the direction of the candidate’s thesis dissertation

advisor, Dr. Qing Hu, Department of Information Technology and Operations

Management, and has been approved by the members of his supervisory committee. It

was submitted to the faculty o f The Barry Kaye College of Business and was accepted in

partial fulfillment o f the requirements for the degree of Doctor of Philosophy.

SUPERVISORY COMMITTEE:

Qing Hu, Dissertation Chair

t ^ I U a , x

Paul Hart, Committee Member

TamdraDinev, Committee Member

y { \ L x h lk a /y y ^ S __________

Ethlyn Williams, CommitleelMember

ittee Member Stuart Galup,

Qing Hu, Chair, Department o f Information Technology and

Operations Management

ennis Coates, Dean, The Barry Kaye College of Business

'T ' ' 2 /

Barry Rosson, Dean, Graduate Studies Date

111




ACKNOWLEDGEMENTS

Special thanks to Dr. Qing Hu, Dr. Paul Hart, Dr. Tamara Dinev, Dr. Ethlyn Williams,

and Dr. Stuart Galup for their guidance, feedback, and support.

iv




ABSTRACT

Author: Nathan D. H einze

Title: Why College Undergraduates Intend to Pursue the InformationTechnology Major: A Multi-theoretical Perspective

Institution: Florida Atlantic University

Dissertation Advisor: Dr. Qing Hu

Degree: Doctor of Philosophy

Year 2007

The purpose of this study is to explore environmental and personal factors that

may influence college undergraduates’ decisions to pursue a major in information

technology. These factors include self-efficacy, outcome expectations, personality traits,

interest in computers, attitudes, subjective norm, perceived behavioral control, and

perceived IT job availability. The paper develops and tests a theoretical model based on

social cognitive theory and the theory of planned behavior, and also incorporates the five

factor model of personality. The intent of this study is to extend the social cognitive

theory and theory of planned behavior literatures by expanding their application to IT

career choice issues and by including personality traits as additional factors. The latter

addition also indicates a unique application of the five factor personality model, as it is

rarely used in concert with self-efficacy, outcome expectations, and broad environmental

factors such as job market demands.

A multi-section survey was administered to college undergraduates at four large

universities in the southeastern United States. Analysis of the data results showed that

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college undergraduates with positive attitudes towards the IT major and high perceived

behavioral control regarding the IT major had a greater intention of pursuing IT. The

study also found positive links between 1) computer self-efficacy and self-evaluating

outcome expectations regarding an IT career, 2) computer self-efficacy and perceived

behavioral control, and 3) self-evaluating outcome expectations and attitudes towards the

IT major. In addition, personality traits moderated a number of model relationships.

Finally, it was found that males were significantly more likely to choose IT than females

and scored higher on computer self-efficacy, self-evaluating outcome expectations,

attitudes towards the IT major, interest in computers, and perceived behavioral control.

The results and contributions of the study are discussed and recommendations are made

for future research.

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TABLE OF CONTENTS

CHAPTER ONE: INTRODUCTION........................................................................ 1

1.1 Statement of the Problem ...................................................................................... 1

1.2 Rationale for the Study......................................................................................... 2

1.3 Research Questions................................................................................................ 6

1.3.1 Environmental Variables.............................................................................. 7

1.3.2 Personal Variables......................................................................................... 8

1.4 Significance of the Study.. .. ................................................................................ 12

1.5 Scope of the Study ................................................................................................. 13

1.6 Chapter One S ummary.......................................................................................... 13

CHAPTER TWO: LITERATURE REVIEW ........................................................... 15

2.1 Introduction............................................................. 15

2.2 Social Cognitive Theory.......................................................................................

162.3 Theory of Planned Behavior ................................................................................. 24

2.4 Combining SCT and TPB ..................................................................................... 28

2.5 Research on Personality........................................................................................ 30

2.5.1 The Five Factor Model................................................................................. 30

2.5.2 Holland’s Theory.......................................................................................... 34

2.5.3 FFM/Holland Overlap................................................................................. 36

2.6 Chapter Two Summary......................................................................................... 39

CHAPTER THREE: THEORY AND HYPOTHESES DEVELOPMENT 40

3.1 Introduction.............................................................................. 40

3.2 Main Theoretical Constructs................................................................................. 41

3.2.1 Computer Self-Efficacy............................................................................... 42

3.2.2. Self-Evaluating Outcome Expec tations ................................................... 43

3.2.3 Attitudes......................................................................................................... 46

3.2.4 Subjective Norm ................................ 47

3.2.5 Perceived Behavioral Contro l...................................................................... 48

3.2.6 Intention to Pursue the IT Major ................................................................ 49

3.3 Moderating Constructs..........................................................................................

493.3.1 Personality...................................................................................................... 49

3.3.1.1 Extraversion.......................................................................................... 51

3.3.1.2 A greeableness....................................................................................... 51

3.3.1.3 Openness................................................................................................ 51

3.3.2 Interest in Computers.................................................................................... 52




3.3.3 Perceived IT Job Availability ............................... 52

3.4 Research Model Development............................................................................. 53

3.5 Research Hypotheses............................................................................................. 60

3.6 Research Design..................................................................................................... 75

3.7 Measurement Instrumen t.......................................................................................

773.8 Chapter Three Summary....................................................................................... 84

CHAPTER FOUR: RESULTS AND ANALYSIS.................................................. 85

4.1 Introduction............................................................................................................. 85

4.2 Data Sample............................................................................................................ 85

4.3 Descriptive Statistics.............................................................................................. 87

4.4 Gender Differences....................... 87

4.5 Scale Reliability..................................................................................................... 91

4.6 Model Testing......................................................................................................... 92

4.6.1 Measurement Model..................................................................................... 92

4.6.1.1 Convergent Validity and Item Reliability ......................................... 93

4.6.1.2 Model Fit and Unidimensionality...................................................... 94

4.6.1.3 Discriminant Validity and Composite Reliability........................... 97

4.6.1.4 Model Invariance................................................................................. 99

4.6.2 Structural Model and Moderator Effects ................................................... 102

4.7 Hypotheses Testing Resu lts................................................................................. I l l

4.7.1 Main Variables............................................................................................... I l l

4.7.2 Moderating Variables....................................................................................

1134.8 Chapter Four Summary......................................................................................... 116

CHAPTER FIVE: DISCUSSION AND CONTRIBUTIONS................................ 117

5.1 Discussion ............................................................................................................... 117

5.1.1 The Importance o f Self-Efficacy................................................................. 117

5.1.2 Outcome Expectations and A ttitudes.......................................................... 120

5.1.3 Subjective Norm ........................................................................................... 122

5.1.4 The Gender D ivide ........................................................................................ 123

5.1.5 The Role o f Personality................................................................................ 125

5.1.6 The Impact of the Job M arket..................................................................... 128

5.2 Contributions.......................................................................................................... 130

5.2.1 Contributions to Theory................................................................................ 130

5.2.2 Contributions to Practice.............................................................................. 132

5.3 Study Limitations................................................................................................... 138




5.4 Future Research..................................................................................................... 139

5.5 Conclusion.............................................................................................................. 141

APPENDICES............................................................................................................... 143

A Questionnaire......................................................................................................

143B Pilot Questionnaire.............................................................................................. 146

C Measurement Model Goodness o f Fit Statistics.............................................. 151

D Structural Model Goodness of Fit Statistics.................................................... 152

E Measurement Model Complete Output............................................................ 153

F Structural Model Complete Ou tput.................................................................. 170

G LISREL Code for Multi-Group Analysis........................................................ 198

BIBLIOGRAPHY ......................................................................................................... 200

BIOGRAPHICAL SKETCH......................................................................................

232




LIST OF TABLES

Table 1: Personality Types.......................................................................................... 34

Table 2: Scales and Items............................................................................................. 79

Table 3: Dem ographics................................................................................................ 87

Table 4: Scale Means and Gender Differences......................................................... 88

Table 5: Scale Reliability............................................................................................ 91

Table 6: Parameter Estimates, Error Terms, T-values, and R-squared Fig ures... 93

Table 7: Measurement Model F it............................................................................... 94

Table 8: Descriptive Statistics and Correlation Matrix ........................................... 99

Table 9: Invariance between High and Low Moderator Groups ............................ 100

Table 10: Structural Model Fit .................................................................................... 102

Table 11: Moderator Variable Groups....................................................................... 105

Table 12: Path Coefficients for Moderating Variables...........................................

106Table 13: Path Significance and Explanatory Power of MainConstructs I l l

Table 14: Moderating Hypotheses Results .............................................................. 113




LIST OF FIGURES

Figure 1: SCT Conceptual Model.......................... 17

Figure 2: The Theory of Planned Behavior .............................................................. 26

Figure 3: Conceptual Model....................................................................................... 41

Figure 4: Research Model........................................................................................... 54

Figure 5: Personality Characteristics by Gender ..................................................... 89

Figure 6: Scale Means by Gender .............................................................................. 90

Figure 7: Measurement Model Standardized Residuals Q-plot............................. 97

Figure 8: Structural Model.......................................................................................... 103

Figure 9: Structural Model Standardized Residuals Q-plot.................................... 103

Figure 10: Perceived IT Job Availability .................................................................. 107

Figure 11: Interest in Computers............................................................................... 108

Figure 12: Extraversion................................................................................................ 109

Figure 13: Agreeableness............................................................................................ 110

Figure 14: Openness..................................................................................................... 110




CHAPTER 1: INTRODUCTION

1.1 Statement of the Problem

During the past five years, a broad decline in computer and technology related

enrollments has occurred at colleges across the United States (Gibson, 2005; Murphy,

2005). Although IT enrollments increased during the dot com boom of the late 1990’s,

they began declining with the dot com fallout and the U.S. economic recession following

the terrorist attacks on 9/11 (National Bureau of Economic Research, 2003). Increasing

levels of international outsourcing have also been cause for concern among those in the

IT field (Malykhina, 2004) and may contribute to the dropping enrollment numbers, as

prospective college students fear their future jobs might be shipped offshore (Diamond

Cluster 2005 Global IT Outsourcing Study; Malykhina, 2004).

Even as recently as 2004, employment data for the IT industry was sluggish

(Srivastava & Theodore, 2004). However, with the rebounding economy, the IT job

market has rebounded. After losing jobs in the years 2001-2003, the industry started

adding jobs in 2004 (ITAA, 2004). The years since have seen strong growth in the field

(Maguire, 2006), and the U.S. now faces a lack of skilled IT workers. To compound the

problem, immigration restric tions mean fewer skilled foreign workers are available to

contribute to U.S. brainpower and skill in technical fields (Ante, 2004). Despite the

upswing in the IT job market, U.S. colleges have not seen a discernable recovery of

student'enrollment in IT related majors. This may contribute to a shortage of technically

skilled workers in the near future and beyond.

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The problem of IT worker shortages may be one that lasts well into the future, as

the U.S. Bureau of Labor projects a number of IT jobs to grow at a “faster” or “much

faster” rate than average through at least the year 2014 (Bureau of Labor Statistics, 2004).

Expected high growth areas include system administration, computer support, systems

analysis, software engineering, computer science, and database administration. These

careers are filled primarily by employees with at least a bachelor’s degree (Bureau of

Labor Statistics, 2004), meaning it is important for universities to graduate students with

information technology degrees.

This pressing need highlights the utility of exploring why students choose to

pursue IT as a major. As the jo b market demand for IT professionals inevitably continues

the cycle of rise and fall in future years, knowledge regarding factors that contribute to

student career choices will be valuable as a resource for understanding why students

choose IT as a career. An increased understanding of this question may provide some

insights into what steps can be taken to encourage increased IT interest and enrollments.

1.2 Rationale for the Study

The topic of career choice has received a great deal of interest within the research

community. A host of variables have been linked to career choice, including personality

(Holland, 1973; Gottfredsson, 1981), parenting (Jodi, Michael, Mlanchuk, Eccles, &

Sameroff, 2001), role models (Karunanayake & Nauta, 2004; Nauta & Kokaly, 2001),

ethnicity (Tang, Fouad & Smith, 1999; Turner & Lapan, 2003), coursework (Trusty,

2002), interests and abilities (Tracey & Hopkins, 2001), socioeconomic status (Bandura,

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Berbaranelli, Caprara, & Pastorelli, 2001; Rojewski & Yang, 1997), social context

(Higgins, 2001), and culture (McWhirter, Hackett, & Bandalos, 1998).

Although quite diverse, variables relating to career choice are increasingly being

viewed through the lens of social cognitive theory (Lent, Brown, & Hackett, 1994).

Social cognitive theory posits that behavior is influenced by a combination of personal

and environmental variables (Bandura, 1986). After the introduction of social cognitive

theory (SCT) as a way to explain career choice (Lent et al., 1994), dozens of studies have

employed SCT using different environmental factors and testing within specific domains.

Environmental factors used within an SCT framework include family influence

(Berrios-Allison, 2005; Jodi et al., 2001), role models (Karunanayake & Nauta, 2004;

Nauta & Kokaly, 2001), social rela tionships (Higgins, 2001), encouragement by others

(Compeau & Higgins, 1995), and social supports and barriers (Lent, 1994; Lent et al.,

2001).

Domains in which SCT has been validated in regards to career issues include

teaching (Mau & Mau, 2006), math (Gainor & Lent, 1998), math and science (Feery,

Fouad, & Smith, 2000; Fouad, Smith, & Zao, 2002; Turner, Steward, & Lapan, 2004),

engineering (Lent et al., 2003), academic research (Bieschke et al., 2004) and art, social

science and English (Fouad et al., 2002).

SCT has also been used successfully to help explain career intentions and choices

of specific demographic groups, such as gays and lesbians (Biescke, Eberz, Bard, &

Croteau, 1998; Morrow, Gore, & Campbell, 1996), Mexican-American and European-

American high school students (McWhirter, 1997; McWhirter et al., 1998),

undergraduate college students (Kanta, 1997), women (Swanson & Woitke, 1997),

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women of color (Byars, 1998), college students (Luzzo, Hasper, Albert, Bibby, &

Martinelli, 1999), and black college students (Gainor & Lent, 1998). However, none of

these studies have addressed information technology career choices, either at the college

or career levels. The question regarding what factors influence a student to pursue the IT

major has yet to be explored, but the large number of studies validating social cognitive

theory in the career choice context lends support to the idea that SCT may be able to help

answer the question.

Although SCT has proven to be quite useful in the area of career intentions and

choice, it does not take into account some important factors that appear to influence

career intentions; behavioral intentions (such as the intention to pursue a particular major

or career) often rely heavily upon perceptions of the behavior’s difficulty level

(“perceived behavioral control”) and attitudes towards the behavior itself (Ajzen, 1991).

Perceived behavioral control and attitudes are two of the key constructs in the theory of

planned behavior, a theory that has shown promise in its ability to predict human

behavior in a wide range of settings (Armitage, 2005; Ajzen & Driver, 1992; Beck &

Ajzen, 1991; Bagozzi, Moore, & Leone, 2004; Eagly & Chaiken, 1993), including career

choice (Arnold et al., 2006; Giles & Rea, 1999).

Arnold et al. (2006) used TPB to help explain the intention to work in the nursing

profession. The researchers found strong support for the predictive ability of the three

core TPB variables of attitude, subjective norm, and perceived behavioral control. Giles

and Rae (1999) successfully employed TPB to investigate why men are less willing to

pursue sex-atypical careers than women and to determine what factors may explain this

reluctance. Operating in a related domain, Shevlin and Millar (2006) used TPB in an

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attempt to predict the longitudinal growth of career exploratory behavior in teenagers,

and concluded that TPB is a useful theoretical framework from which to explain career

information-seeking behavior.

There have as of yet been no studies using TPB to explain IT career decisions.

Most of the TPB based studies in the IT literature have focused on technology use as the

dependent variable. Brown and Venkatesh (2005) used TPB as the theoretical framework

for exploring technology adoption within households, and Taylor and Todd (1995)

compared TPB and a decomposed TPB model to the technology acceptance model

(TAM) in order to see which best predicted IT usage. TPB has also been used in studies

on music piracy (d’Astous, Colbert, & Montpetit, 2005), internet purchases (George,

2004) online banking (Tan & Teo, 2000), decision support system use (Workman, 2005),

and gender/age influences on technology use (Morris, Venkatesh, & Ackerman, 2005).

Despite the support for the use of SCT and TPB to predict career intentions, no

discussion of the issue is complete without acknowledging the prominent role that

personality has played in predicting career choice (Holland, 1973; Gottfredsson, 1981).

Numerous studies have found personality (or more specifically, personality-derived

interests) to be a key factor in both choice of a career and satisfaction with a career

(Spokane & Cruza-Guet, 2005). On a related note, Barrick and Mount (1991) found

correlations between personality and job performance in a number of occupational

categories, and Mount, Barrick, and Stewart (1991) found correlations between

personality traits and job performance. Therefore, in addition to using TPB to further

increase the explanatory power of SCT, it is advisable to explore the role that personality

may play in conjunction with these two theories.

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In summary, social cognitive theory has been used successfully to explain career

choices in many situations. However, other factors have also been shown to possibly play

an influential role, including attitudes, perceived behavioral control, and personality. It is

thus the goal of this study to combine factors from SCT, TPB, and personality theory to

create a model that helps to explain the intention to pursue IT as a major.

1.3 Research Questions

The chief research question this study will address is why college undergraduates

intend or do not intend to pursue IT as their major. Therefore, the dependent variable is

behavioral in nature (Figure 1). The model proposes that the answer, as suggested by

SCT, lies in environmental and personal variables that interactively influence behavior

(Figure 1). The interaction among these variables should help to answer questions

regarding how the process takes place.

Specifically, the study will investigate what, how, and why personal and

environmental factors affect the intention to pursue information technology as a major.

The independent variables are personal and environmental factors that may influence

intentions. Based on this framework, we can address some broad issues, the two most

obvious categories being 1) what, how, and why environmental factors influence the

intent to pursue IT as a major, and 2) what, how, and why personal factors influence the

intent to pursue IT as a major. The following two sections will examine environmental

and personal variables that may be of interest in light of the study’s purposes.

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1.3.1 Environmental Variables

Of the three key TPB variables shown to influence behavioral intention,

subjective norm is the one that is environmental in nature. Subjective norm refers to the

social pressures an individual perceives regarding whether a behavior (pursuing the IT

major) should or should not be performed (Ajzen, 1991). It deals with the support an

individual receives from his or her immediate social environment, e.g., friends and

family. Many studies within the IT field have indicated subjective norm has little or no

impact upon behavioral intention (Chau & Hu, 2001; Dinev & Hu, 2007; George, 2004;

Tan & Teo, 2000; Taylor & Todd, 1995), and it is considered to be the weakest of the

core TPB variables in predicting behavioral intention (Armitage & Conner, 2001).

However, there have been few studies using TPB to explain career choices, and studies in

previously covered domains may not be indicative of subjective norm’s importance in the

field of career intentions. In fact, subjective norm is very similar to the idea of social

support (Lent et al., 2001), which has been found to be a strong predictor of career

outcomes (Lent et al., 2003a). Therefore, a key issue this study will address is whether or

not subjective norm influences behavioral intention in the realm of major/career choice.

Macro environmental factors such as the IT job market, the economy as a whole,

outsourcing, and other broad factors that may influence an IT career decision are not as

well researched, although at least within the IT industry itself there seems to be major

concern among employees about outsourcing and job security (Diamond Cluster 2005

Global IT Outsourcing Study). The question to be asked is if and how these factors affect

a student’s perceptions of the likelihood of finding meaningful employment; and how

these perceptions are then related to a student’s intention to choose IT.

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For example, if the current state of the IT job market is perceived as weak by

those considering choosing IT as a career, then we would expect this perception to have a

negative impact on IT enrollments. Although no studies are available that specifically

address the IT job market, overall labor market demands do have an impact upon both

community college and university enrollments (Betts & McFarland, 1995; Dellas &

Sakellaris, 2003; Paulsen & Pogue, 1988). As well, the high amount of press coverage

regarding international outsourcing may contribute to poor perceptions of the IT job

market, while the truth is that the future of IT in the United States is improving

(Frauenheim, 2005; Jacobs, 2006).

1.3.2 Personal Variables

Personal factors may also influence the choice of a major, possibly by influencing

an individual’s preference for activities associated with different types of careers. In fact,

vocational theories posit that personality is generally the single most influential

determinant of career choice (Holland, 1973; Gottfredsson, 1981). In other words, the

reason why an individual chooses a career may be closely related to personality.

A common problem with personality research is that personality is a broad and

multi-faceted concept that has been defined and measured in many ways (McAdams,

1995); in fact, the measurement of “personality” that has been studied the most in

relationship to career choices (Holland’s theory of personality style and career

development), is more accurately described as a measure of interests that are based on

personality (Holland, 1997). The lack of research linking underlying personality traits to

career choices may be due to the fact that only recently has there been any level of

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consensus regarding what basic traits actually comprise personality (John & Srivastava,

1999). However, the field of psychology has come to a fairly broad consensus on an

accepted taxonomy of personality traits composed of five broad dimensions (Digman,

1990): extraversion, agreeableness, conscientiousness, neuroticism, and openness.

The stability of this five factor model (FFM) or “Big Five” has been verified on

numerous occasions and in a number of settings (Digman & Inouye, 1986: Goldberg,

1990: Nollar, Law, & Comrey, 1987; Saucier & Goldberg, 1998). Soldz & Vaillant

(1999) conducted a longitudinal study over 45 years and established that the factors are

remarkably stable even over such an extended period of time. The five factors are also

considered to be universal in nature, applying to a diverse array of cultures (McCrea &

Costa, 1997). Because of this strong theoretical base, it is possible to more confidently

apply personality theory to career choice issues. Therefore, this study will attempt to

discover what, if any, impact personality traits have on students’ decisions to pursue IT as

their major.

TPB includes two personal factors that appear to be predictive of behavioral

intention in a wide variety of contexts (Armitage & Conner, 2001): perceived behavioral

control and attitudes (Ajzen & Driver, 1992; Beck & Ajzen, 1991). Perceived behavioral

control (PBC) is the difficulty an individual associates with the behavior in question, in

this case the difficulty of the IT major. Attitudes are the attitudes a student has towards

the IT major. Because of the preponderance of evidence linking PBC and attitudes with

behavioral intent (Armitage & Conner, 2001), it is important to explore how much impact

1) perceived behavioral control regarding the IT major and 2) attitudes toward the IT

major have on the intention to pursue IT.

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Two further questions are addressed by this study; namely 1) what is the role of

outcome expectations on the intention to choose IT, and 2) what is the role of self-

efficacy? Studies show that positive outcome expectations for a particular occupation

results in increased interest and exploratory behavior related to the occupation, along

with an increase in the likelihood of choosing the occupation (Hackett, Betz, & Casas,

1992; Lent, Lopez, & Bieschke, 1991, 1993; Fouad & Smith, 1996). Betz & Voyten

(1997) reported that outcome expectations were good predictors of academic and career

indecision and exploration intent, and Diegelman and Subich (2001) found that outcome

expectations related positively and significantly to interest in a particular major.

Therefore, it appears that outcome expectations are connected with behavior and

behavioral intentions.

Outcome expectations have also been shown to be positively and consistently

correlated with self-efficacy (Betz & Voyten, 1997; Diegelman & Subich, 2001; Fouad &

Smith, 1996; Lent et al., 1991, 1993), reflecting Bandura’s (1986) assertion that outcome

expectations are influenced by efficacy beliefs. Research has supported this claim, with

higher levels of self-efficacy resulting in higher levels of outcome expectations

(Compeau & Higgins, 1995; Fouad & Smith, 1996; Lent et al., 2003). Lent et al. (1994)

found that self-efficacy and outcome expectations both appear to influence educational

and vocational choices and success. In sum, the bulk of the evidence indicates that self-

efficacy influences outcome expectations, and outcome expectations influence behavioral

intentions. This study will seek to understand if this linkage holds true when applied to

choice of the IT major.

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In addition to its connection with outcome expectations, Self-efficacy has been

found to correlate with TPB via its impact on perceived behavioral control. Pavlou and

Fygenson (2007) found that self-efficacy and PBC were significantly related. Taylor and

Todd (1995) modeled self-efficacy as an antecedent to PBC and also found a significant

relationship between the two. Therefore, this study will also try to assess whether self-

efficacy’s importance to perceived behavioral control applies to career choice.

The final question this study seeks to answer is what role interests may play in the

selection of a major. Interests are central to social cognitive career theory, and have been

shown to be a predictor of career choice (Lent, Brown, & Hackett, 2000; Lent, Hill, &

Hoffman, 2003). They are also the basis for Holland’s theory of personality style and

career development (1966, 1973, 1979, 1987), a theory that specifically links interests

with career choice and satisfaction. In this study, interests will be defined as interest in

computer work, as computer work is an attribute closely associated with IT. The goal is

to examine whether interest in computers has an impact on the intention to purse IT as a

major.

In summary, this study will present and test a model examining why students

choose or do not choose IT as a major. The model is based on the theoretical foundations

of social cognitive theory and the theory of planned behavior, and proposes that

environmental and personal variables may interactively influence the intention to pursue

IT as a major. SCT and TPB have not yet been combined in a study looking at career

issues, and it is argued that TPB may be useful in this area. Finally, this study seeks to

discover whether personality, interest in computers, and perceived IT job availability

might contribute significantly to the model.

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1.4 Significance of the Study

This study will contribute to the body of research by utilizing an SCT framework

that houses a TPB-based model, as well as additional environmental variables (perceived

IT job availability and interest in computers) and personality factors. As such, it

incorporates many of the factors shown to impact career choice into a single

comprehensive model. This use of TPB within an SCT framework is a unique approach

to addressing career choice issues, and one that may offer increased explanatory and

predictive power than models using only SCT. Individually, SCT has been shown to be a

powerful predictor of career choice (Lent et al., 1994; Lent, 2005; Sadri & Robertson,

1993; Stajkovic & Luthans, 1998), and TPB has shown promise in its ability to predict

career intentions (Arnold et al., 2006; Giles & Rae, 1999) as well as human behavior in a

wide range of settings (Armitage, 2005; Ajzen & Driver, 1992; Beck & Ajzen, 1991;

Bagozzi et al., 2004; Eagly & Chaiken, 1993). Therefore, a model combining the two

may offer a great deal of utility in helping to predict and understand career choice.

An increased understanding of career intentions will also have practical

significance. In regards to the problem of low IT enrollments and the related demand for

more IT workers in the U.S. market (Bureau of Labor Statistics, 2004), the more

information that is available, the more likely it is that corrective action can be taken. In

short, a problem can be fixed more easily if it is understood. Therefore, it is important to

understand the relative impact of factors that may affect s tudents’ choice of IT. Once a

pattern of affect emerges, it will be possible to discuss corrective action that can be taken

by parents, students and educators alike.

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Why College Undergraduates Intend to Pursue the Information Technology Major

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