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Utilizing Structural Equation Modeling and Social Cognitive Career Theory to Identify Factors in Choice of IT as a Major

Published: 17 September 2014 Publication History

Abstract

In the United States, the number of students entering into and completing degrees in science, technology, engineering, and mathematics (STEM) areas has declined significantly over the past decade. Although modest increases have been shown in enrollments in computer-related majors in the past 4 years, the prediction is that even in 3 to 4 years when these students graduate, there will be shortages of computer-related professionals for industry. The challenge on which this article focuses is attracting students to select an information technology (IT) field such as computer science, computer engineering, software engineering, or information systems as a major when many high schools do not offer a single computer course, and high school counselors, families, and friends do not provide students with accurate information about the field. The social cognitive career theory (SCCT) has been used extensively within counseling and career psychology as a method for understanding how individuals develop vocational interests, make occupational choices, and achieve success within their chosen field. In this article, the SCCT model identifies factors that specifically influence high school students to select a major in an IT-related discipline. These factors can then be used to develop new or enhance existing IT-related activities for high school students. Our work demonstrates that both interest and outcome expectations have a significant positive impact on choice to major. Interest also is found to mediate the effects of self-efficacy and outcome expectations on choice of major. Overall, the model predicts a good portion of variance in the ultimate outcome of whether or not an individual chooses to major in IT.

References

[1]
C. Aasheim, L. Li, and S. Williams. 2009. Knowledge and skill requirements for entry-level information technology workers: A comparison of industry and academia. Journal of Information Systems Education 20, 3, 349.
[2]
R. Agarwal, V. Sambamurthy, and R. M. Stair. 2000. The evolving relationship between general and specific computer self-efficacy: An empirical assessment. Information Systems Research 11, 4, 418--430.
[3]
A. Akbulut, C. Looney, and J. Motwani. 2008. Combating the decline in information systems majors: The role of instrumental assistance. Journal of Computer Information Systems 48, 3, 84.
[4]
A. Y. Akbulut-Bailey. 2011. The role of contextual support in increasing information systems enrollments. In Digital Enterprise and Information Systems. Communications in Computer and Information Science, Vol. 194. Springer, 87--98.
[5]
A. Aken and M. D. Michalisin. 2007. The impact of the skills gap on the recruitment of MIS graduates. In Proceedings of the 2007 ACM SIGMIS CPR Conference on Computer Personnel Research: The Global Information Technology Workforce (SIGMIS CPR'07). ACM, New York, NY, 105--111.
[6]
R. Babin, K. Grant, and L. Sawal. 2008. Identifying influencers in high school student ICT career choice. Information Systems Education Journal 8, 26, 1--18.
[7]
R. Bagozzi and T. F. Heatherton. 1994. A general approach to representing multifaceted personality constructs: Application to state self-esteem. Structural Equation Modeling: A Multidisciplinary Journal 1, 1, 35--67.
[8]
R. Bagozzi and Y. Yi. 1988. On the evaluation of structural equation models. Journal of the Academy of Marketing Science 16, 1, 74--94.
[9]
A. Bandura. 1986. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall, Englewood Cliffs, NJ.
[10]
A. Bandura. 1989. Human agency in social cognitive theory. American Psychologist 44, 9, 1175--1184.
[11]
A. Bandura. 1997. Self-Efficacy: The Exercise of Control. Freeman, New York, NY.
[12]
A. Bandura. 2001. Social cognitive theory: An agentic perspective. Annual Review of Psychology 52, 1, 1--26.
[13]
W. O. Bearden, R. G. Netemeyer, and M. F. Mobley. 1993. Handbook of Marketing Scales: Multi-Item Measures for Marketing and Consumer Behavior Research. Sage Publications, Newbury Park, CA.
[14]
M. C. Berger. 1988. Predicted future earnings and choice of college major. Industrial and Labor Relations Review 41, 3, 418--429.
[15]
G. W. Bock and Y. G. Kim. 2002. Breaking the myths of rewards: an exploratory study of attitudes about knowledge sharing. Information Resources Management Journal 15, 2, 14--21.
[16]
G. W. Bock, R. W. Zmud, Y. Kim, and J. Lee. 2005. Behavioral intention formation knowledge sharing: Examining roles of extrinsic motivators, social--psychological forces, and organizational climate. MIS Quarterly 29, 1, 87--111.
[17]
M. W. Browne and R. Cudeck. 1993. Alternative Ways of Assessing Model Fit. Sage, Newbury Park, CA.
[18]
Bureau of Labor Statistics. 2011. Databases, Tables & Calculators by Subject. Retrieved May 14, 2011, from http://www.bls.gov/data/.
[19]
D. P. Campbell. 1971. Handbook for the Strong Vocational Interest Blank. Stanford University Press, Stanford, CA.
[20]
D. P. Campbell. 1995. The Campbell Interest and Skill Survey (CISS): A product of ninety years of psychometric evolution. Journal of Career Assessment 3, 4, 391--410.
[21]
L. Carter. 2006. Why students with an apparent aptitude for computer science don't choose to major in computer science. In Proceedings of the 37th SIGCSE Technical Symposium on Computer Science Education (SIGCSE'06). ACM, New York, NY, 27--31.
[22]
N. Cherry. 1975. Occupational values and employment: A follow-up study of graduate men and women. Higher Education 4, 3, 357--368.
[23]
W. W. Chin. 1998. Commentary: Issues and opinion on structural equation modeling. MIS Quarterly 22, 1, vii--xvi.
[24]
Committee on Prospering in the Global Economy of the 21st Century: An Agenda for American Science and Technology, National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. 2007. Rising above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future. National Academies Press, Washington, DC.
[25]
D. R. Compeau and C. A. Higgins. 1995a. Application of social cognitive theory to training for computer skills. Information Systems Research 6, 2, 118--143.
[26]
D. R. Compeau and C. A. Higgins. 1995b. Computer self-efficacy: Development of a measure and initial test. MIS Quarterly 19, 2, 189--211.
[27]
G. B. Cunningham, J. Bruening, M. L. Sartore, M. Sagas, and J. S. Fink. 2005. The application of social cognitive career theory to sport and leisure career choices. Journal of Career Development 32, 2, 122--138.
[28]
R. V. Dawis and L. H. Lofquist. 1984. A Psychological Theory of Work Adjustment: An Individual-Differences Model and Its Applications, University of Minnesota Press, Minneapolis, MN.
[29]
G. DeSanctis. 1983. Expectancy theory as an explanation of voluntary use of a decision-support system. Psychological Reports 52, 1, 247--260.
[30]
E. E. Diamond and D. G. Zytowski. 2000. The Kuder Occupational Interest Survey. Lawrence Erlbaum Associates, Mahwah, NJ.
[31]
J. Farley and O. Staniec. 2004. The effects of race, sex, and expected returns on the choice of college major. Eastern Economic Journal 30, 4, 549--563.
[32]
S. Felton, N. Buhr, and M. Northey. 1994. Factors influencing the business student's choice of a career in chartered accountancy. Issues in Accounting Education 9, 1, 131--141.
[33]
C. Fornell and D. F. Larcker. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 1, 39--50.
[34]
N. A. Fouad and P. L. Smith. 1996. A test of a social cognitive model for middle school students: Math and science. Joumal of Counseling Psychology 43, 3, 338--346.
[35]
K. A. Gainor and R. W. Lent. 1998. Social cognitive expectations and racial identity attitudes in predicting the math choice intentions of black college students. Journal of Counseling Psychology 45, 4, 403--413.
[36]
D. Gefen and D. W. Straub. 2005. A practical guide to factorial validity using PLS-graph: Tutorial and annotated example. Communications of the Association for Information Systems 16, 1, 5.
[37]
D. Gefen, D. W. Straub, and M. C. Boudreau. 2000. Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems 4, 1, 7.
[38]
M. J. Ginzberg. 1981. Early diagnosis of MIS implementation Failure: Promising results and unanswered questions. Management Science 27, 4, 459--478.
[39]
M. Granger, G. Dick, C. Jacobson, and C. Slyke. 2007. Information systems enrollments: Challenges and strategies. Journal of Information Systems Education 18, 3, 303--311.
[40]
G. Hackett and N. E. Betz. 1981. A self-efficacy approach to the career development of women. Journal of Vocational Behavior 18, 3, 326--339.
[41]
G. Hackett and R. W. Lent. 1992. Theoretical Advances and Current Inquiry in Career Psychology, Wiley, New York, NY.
[42]
J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, and R. L. Tatham. 2006. Multivariate Data Analysis. Pearson Education, Upper Saddle River, NJ.
[43]
J. C. Hansen. 2000. Interpretation of the Strong Interest Inventory. Erlbaum, Mahwah, NJ.
[44]
J. C. Hansen. 2005. Assessment of Interests. Wiley, New York, NY.
[45]
L. W. Harmon and F. H. Borgen. 1995. Advances in career assessment and the 1994 Strong Interest Inventory. Journal of Career Assessment 3, 4, 347--468.
[46]
L. W. Harmon, J. C. Hansen, F. H. Borgen, and A. L. Hammer. 1994. Strong Interest Inventory: Applications and Technical Guide. Consulting Psychologists Press, Palo Alto, CA.
[47]
N. Heinze and Q. Hu. 2009. Why college undergraduates choose IT: A multi-theoretical perspective. European Journal of Information Systems 18, 5, 462--475.
[48]
T. Hill, N. D. Smith, and M. F. Mann. 1987. Role of efficacy expectations in predicting the decision to use advanced technologies: The case of computers. Journal of Applied Psychology 72, 2, 307--313.
[49]
J. L. Holland. 1959. A theory of vocational choice. Journal of Counseling Psychology 6, 1, 35--45.
[50]
J. L. Holland. 1997. Making Vocational Choices: A Theory of Vocational Personalities and Work Environments. Psychological Assessment Resources, Odessa, FL.
[51]
L. Hu and P. M. Bentler. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6, 1, 1--55.
[52]
X. Hu, Z. Lin, A. B. Whinston, and H. Zhang. 2004. Hope or hype: On the viability of escrow services as trusted third parties in online auction environments. Information Systems Research 15, 3, 236--249.
[53]
W. W. Huang, J. Greene, and J. Day. 2008. Outsourcing and the decrease of IS program enrollment. Communications of the ACM 51, 6, 101--104.
[54]
R. D. Johnson and G. M. Marakas. 2000. The role of behavioral modeling in computer skills acquisition: Toward refinement of the model. Information Systems Research 11, 4, 403--417.
[55]
D. Kim, F. S. Markham, and J. D. Cangelosi. 2002. Why students pursue the business degree: A comparison of business majors across universities. Journal of Education for Business 78, 1, 28.
[56]
S. S. Kim and J.-Y. Son. 2009. Out of dedication or constraint? A dual model of post-adoption phenomena and its empirical test in the context of online services. MIS Quarterly 33, 1, 49--70.
[57]
J. D. Krumboltz, A. M. Mitchell, and G. B. Jones. 1976. A social learning theory of career selection. Counselling Psychologist 6, 1, 71--81.
[58]
F. Kuder and D. G. Zytowski. 1991. Kuder Occupational Interest Survey: General Manual. McGraw-Hill, Monterey, CA.
[59]
R. W. Lent. 2005. A Social Cognitive View of Career Development and Counseling. Wiley, New York, NY.
[60]
R. W. Lent and S. D. Brown. 2006. Integrating person and situation perspectives on work satisfaction: A social-cognitive view. Journal of Vocational Behavior 69, 236--247.
[61]
R. W. Lent, S. D. Brown, and G. Hackett. 1994. Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior 45, 79--122.
[62]
R. W. Lent, S. D. Brown, and G. Hackett. 2000. Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counseling Psychology 47, 1, 36--49.
[63]
R. W. Lent, S. D. Brown, J. Schmidt, B. Brenner, H. Lyons, and D. Treistman. 2003. Relation of contextual supports and barriers to choice behavior in engineering majors: Test of alternative social cognitive models. Joumal of Counseling Psychology 50, 4, 458--465.
[64]
R. W. Lent, S. D. Brown, H.-B. Sheu, J. Schmidt, B. R. Brenner, C. S. Gloster, G. Wilkins, L. C. Schmidt, H. Lyons, and D. Treistman. 2005a. Social cognitive predictors of academic interests and goals in engineering: Utility for women and students at historically black universities. Journal of Counseling Psychology 52, 1, 84--92.
[65]
R. W. Lent, A. M. Lopez, F. G. Lopez, and H.-B. Sheu. 2008. Social cognitive career theory and the prediction of interests and choice goals in the computing disciplines. Journal of Vocational Behavior 73, 1, 52--62.
[66]
R. W. Lent, D. Singley, and H.-B. Sheu. 2005b. Social cognitive predictors of domain and life satisfaction: Exploring the theoretical precursors of subjective well-being. Joumal of Counseling Psychology 52, 3, 429--442.
[67]
R. W. Lent, M. D. C. Taveira, H.-B. Sheu, and D. Singley. 2009. Social cognitive predictors of academic adjustment and life satisfaction in Portuguese college students: A longitudinal analysis. Journal of Vocational Behavior 74, 190--198.
[68]
H.-Y. Liao, P. I. Armstrong, and J. Rounds. 2008. Development and initial validation of public domain basic interest markers. Journal of Vocational Behavior 73, 1, 159--183.
[69]
L. D. Lindley and F. H. Borgen. 2000. Personal style scales of the Strong Interest Inventory: Linking personality and interests. Journal of Vocational Behavior 57, 1, 22--41.
[70]
W. L. Lomerson and L. Pollacia. 2006a. CIS enrollment decline: Examining pre-college factors. In Proceedings of the 2006 Southern Association for Information Systems Conference. Paper 17.
[71]
W. L. Lomerson and L. Pollacia. 2006b. Declining CIS enrollment: An examination of pre-college factors. Information Systems Education Journal 4, 35, 3--13.
[72]
R. L. Lowman. 2003. Assessment of Interests. Sage, Thousand Oaks, CA.
[73]
R. C. MacCallum, M. W. Browne, and H. M. Sugawara. 1996. Power analysis and determination of sample size for covariance structure modeling. Psychological Methods 1, 2, 130--149.
[74]
A. Majchrzak, C. M. Beath, R. A. Lim, and W. W. Chin. 2005. Managing client dialogues during information systems design to facilitate client learning. MIS Quarterly 29, 4, 653--672.
[75]
C. A. Malgwi, M. A. Howe, and P. A. Burnaby. 2005. Influences on students' choice of college major. Journal of Education for Business 80, 5, 275--282.
[76]
G. M. Marakas, R. D. Johnson, and P. F. Clay. 2007. The evolving nature of the computer self-efficacy construct: An empirical investigation of measurement construction, validity, reliability and stability over time. Journal of the Association for Information Systems 8, 1, 16--46.
[77]
K. Mathieson. 1991. Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research 2, 3, 173--191.
[78]
C. R. McInerney, N. C. Didonato, R. Giagnacova, and A. M. O'Donnell. 2006. Students' choice of information technology majors and careers: A qualitative study. Information Technology, Learning, and Performance Journal 24, 2, 35--53.
[79]
L. K. Mitchell and J. D. Krumboltz. 1990. Social Learning Approach to Career Decision Making: Krumboltz' Theory. Jossey-Bass, San Francisco, CA.
[80]
L. K. Mitchell and J. D. Krumboltz. 1996. Krumboltz's Learning Theory of Career Choice and Counseling. Jossey-Bass, San Francisco, CA.
[81]
I. T. Miura. 1987. The relationship of computer self-efficacy expectations to computer interest and course enrollment in college. Sex Roles 16, 5--6, 303--311.
[82]
K. D. Multon, S. D. Brown, and R. W. Lent. 1991. Relation of self-efficacy beliefs to academic outcomes: A meta-analytic investigation. Journal of Counseling Psychology 38, 1, 30--38.
[83]
L. K. Muthen and B. O. Muthen. 1998--2010. Mplus User's Guide (6th ed). Muthen & Muthen, Los Angeles, CA.
[84]
J. C. Nunnally. 1978. Psychometric Theory. McGraw-Hill, New York, NY.
[85]
D. A. Patterson. 2005. Restoring the popularity of computer science. Communications of the ACM 48, 9, 25--28.
[86]
L. Pollacia and J. Russell. 2007. Addressing the decline in CIS enrollment. Issues in Information Systems 8, 1, 97--102.
[87]
D. J. Prediger and K. B. Swaney. 1995. Using UNIACT in a comprehensive approach to assessment for career planning. Journal of Career Assessment 3, 4, 429--451.
[88]
T. Raykov and D. Grayson. 2003. A test for change of composite reliability in scale development. Multivariate Behavioral Research 38, 2, 143--159.
[89]
C. K. Riemenschneider, D. J. Armstrong, and J. E. Moore. 2009. Meeting the demand for IT workers: A call for research. European Journal of Information Systems 18, 5, 458--461.
[90]
J. A. Rursch, A. Luse, and D. Jacobson. 2010. IT-Adventures: A program to spark it interest in high school students using inquiry-based learning with cyber defense, game design, and robotics. IEEE Transactions on Education 53, 1, 9.
[91]
G. Sadri and I. T. Robertson. 1993. Self-efficacy and work-related behaviour: A review and meta-analysis. Applied Psychology 42, 2, 139--152.
[92]
K. P. Scheibe, B. E. Mennecke, and A. Luse. 2007. The role of effective modeling in the development of self-efficacy: The case of the transparent engine. Decision Sciences Journal of Innovative Education 5, 1, 21--42.
[93]
S. M. Smith. 2002. The role of social cognitive career theory in information technology based academic performance. Information Technology, Learning, and Performance Journal 20, 2, 1--10.
[94]
A. R. Spokane and M. C. Cruza-Guet. 2005. Holland's Theory of Vocational Personalities in Work Environments. Wiley, New York, NY.
[95]
E. K. Strong. 1926. An interest test for personnel managers. Journal of Personnel Research 5, 5, 194--204.
[96]
E. K. Strong. 1943. Vocational Interests of Men and Women. Stanford University Press, Stanford, CA.
[97]
D. E. Super. 1957. The Psychology of Careers. Harper & Row, New York, NY.
[98]
D. E. Super. 1981. A Developmental Theory: Implementing a Self-Concept. Thomas, Springfield, IL.
[99]
D. E. Super. 1990. A Life-Span, Life-Space Approach to Career Development. Jossey-Bass, San Francisco, CA.
[100]
B. Szajna and R. W. Scamell. 1993. The effects of information system user expectations on their performance and perceptions. MIS Quarterly 17, 4, 493--516.
[101]
J. Vegso. 2005. CS bachelor's degree production grows in 2004; poised for decline. Computing Research News 17, 2.
[102]
F. W. Vondracek. 2001. The developmental perspective in vocational psychology. Journal of Vocational Behavior 59, 252--261.
[103]
F. W. Vondracek, R. M. Lerner, and J. E. Schulenberg. 1986. Career Development: A Life-Span Developmental Approach. Erlbaum Associates, Hillsdale, NJ.
[104]
I. B. Weiner, D. K. Freedheim, J. R. Graham, and J. Naglieri. 2003. Handbook of Psychology: Assessment Psychology. John Wiley & Sons, Hoboken, NJ.
[105]
C. Wilson, L. A. Sudol, C. Stephenson, and M. Stehlik. 2010. Running on Empty: The Failure to Teach K-12 Computer Science in the Digital Age. Association for Computing Machinery, New York, NY.
[106]
W. Zhang. 2007. Why IS: Understanding undergraduate students' intentions to choose an information systems major. Journal of Information Systems Education 18, 4, 447--458.
[107]
S. Zweben. 2012. Computing Degree and Enrollment Trends: From the 2010--2011 CRA Taulbee Survey. Computer Research Association. Retrieved April 9, 2012, from http://www.cra.org/uploads/documents/resources/taulbee/CS_Degree_and_Enrollment_Trends_2010-11.pdf.

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      cover image ACM Transactions on Computing Education
      ACM Transactions on Computing Education  Volume 14, Issue 3
      November 2014
      129 pages
      EISSN:1946-6226
      DOI:10.1145/2668970
      Issue’s Table of Contents
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      Publication History

      Published: 17 September 2014
      Accepted: 01 May 2014
      Revised: 01 February 2014
      Received: 01 March 2013
      Published in TOCE Volume 14, Issue 3

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      1. Structural equation modeling
      2. interest
      3. self-efficacy
      4. social cognitive career theory

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