THE INFLUENCE OF UNCONSCIOUS NEEDS ON COLLEGE PROGRAM CHOICE

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University of Massachusetts Amherst ScholarWorks@UMass Amherst Tourism Travel and Research Association: Advancing Tourism Research Globally 2007 ttra International Conference THE INFLUENCE OF UNCONSCIOUS NEEDS ON COLLEGE PROGRAM CHOICE Dr. Xuan Tran Department of Health, Leisure and Exercise Science University of West Florida Dr. Stu Ryan Department of Health, Leisure and Exercise Science University of West Florida Follow this and additional works at: http://scholarworks.umass.edu/ttra Tran, Dr. Xuan and Ryan, Dr. Stu, "THE INFLUENCE OF UNCONSCIOUS NEEDS ON COLLEGE PROGRAM CHOICE" (2016). Tourism Travel and Research Association: Advancing Tourism Research Globally. 32. http://scholarworks.umass.edu/ttra/2007/illustrated_papers/32 This is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Tourism Travel and Research Association: Advancing Tourism Research Globally by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact scholarworks@library.umass.edu.

The Influence of Unconscious Needs on College Program Choice Xuan Tran, Ph.D. Department of Health, Leisure and Exercise Science University of West Florida Stu Ryan, Ph.D. Department of Health, Leisure and Exercise Science University of West Florida ABSTRACT One of the most critical issues for colleges and universities today is to adequately understand why students select one program rather than another. Until the early 1980s, few analysts empirically experimented to find out why students pick the one program over another (Chapman 1981; Fuller, Manski, & Wise 1982; Manski & Wise 1983). Recent research and theory posit that choice of field study depends on anticipated earnings after graduation (Boudarbat 2006; Card 1999). In fact, a program in student s choice depends not only on anticipated earnings but also on their choice interest. However, decision models used for students preference are not available (Behrman, Kletzer, McPherson, & Schapiro 1998; Soss 1974), and the choice process is more complicated in terms of psychology (Hossler et al. 1989). In order to provide good programs for students, different colleges offer a variety of courses based on not only their marketing analyses but more importantly, the students personality. Motivation is the key factor to influence students making decision. This study aimed to explore the relationships between motives and program choices. Data were randomly collected from 277 students at three colleges of the University of West Florida during the fall term 2006. The findings of the study indicated that there were significant relationships between (1) science programs and need for power; (2) business programs and need for affiliation; and (3) hospitality and sport programs and need for achievement. INTRODUCTION Failure to adequately understand why students select one program rather than another is one of the most critical issues at colleges and universities. Until the early 1980s, few analysts empirically experimented to find out why students pick the one program over another (Chapman 1981; Fuller, Manski, & Wise 1982; Manski & Wise 1983). Recent research and theory posit that choice of field study depends on anticipated earnings after graduation (Boudarbat 2006; Card 1999). In fact, a program in student s choice depends not only on anticipated earnings but also on their choice interest. However, decision models used for students preference are not available (Behrman, Kletzer, McPherson, & Schapiro 1998; Soss 1974), and the choice process is more complicated in terms of psychology (Hossler et al. 1989). In order to provide good programs for students, different colleges offer a variety of courses based on not only their marketing analyses 423

but more importantly, the students personality. Motivation is the key factor to influence students making decision. This study aimed to explore the relationships between motives and program choices. STUDY METHODS In order to explore the relationships of motives and program choice, the variables must be measured with appropriate methods. A sample was randomly selected from students at University of West Florida. During the two-week period, 300 questionnaires were randomly distributed in the hospitality and sport class, science class, and business class. The response rate of the questionnaires was 92%. Unconscious motives were measured with the Thematic Apperception Test (TAT) and travel preferences with the self-report questionnaire method because of the following reasons. The TAT, an exercise in which participants create brief imaginative stories in response to pictures, is a test designed to measure the human needs or motivation that people are either unwilling or unable to verbalize because of a lack of conscious awareness (Murray 1943). The test has frequently been used to create measures of implicit motivation based on the assumption that such motives are best measured by spontaneous fantasy as opposed to conscious statements of goals or motives (McClelland 1992; Weinberger & McClelland 1990). The four Murray (1943) TAT pictures were chosen due to four reasons. First, the pictures represented common situations and therefore could better elicit content of interest to all three needs and motive combinations. Second, the pictures with few subjects represented sufficiently low ambiguous levels for a respondent to effectively produce a particular type of motive separate from other motives (Smith 1992). Third, the number of four pictures was large enough to be reliable (Smith 1992). Finally, the four pictures included males and females who were older than the subjects to avoid those people eliciting recollections of past events rather than thoughts that reflect current concerns (McClelland, Atkinson, Clark, & Lowell 1953). The two TAT trained raters reviewed the coding manual and received clarification regarding the meaning of code categories. The raters then scored practice materials, compared their scoring each other, and discussed differences between their scoring and that of an expert s scoring sample to further clarify their understanding of the coding categories based on the scoring TAT manual (Smith 1992, p. 632). In the second part of the survey, subjects were asked to rate their Likert-scale preference from 1 representing a very weak preference to 7 representing very strong preference. Three preference questions were about academic interests including general science, business, and hospitality and sport. The appropriate approach was determined to be one using canonical variate analysis to relate the three motive variables to the three preference variables simultaneously. The "canonical" term indicates that the technique is extracting from a square matrix. Canonical variate analysis simultaneously calculates a linear composite of all motive variables and a linear composite of all brand attribute variables. Unlike other multivariate techniques, these weighted composites are derived in pairs. Each linear combination is called a canonical variate and takes the general linear form. The unconscious motives are considered predictors in the model while the brand attribute variables are considered criterion variables in the model. In order to achieve a 424

stable outcome, the canonical correlations and structure coefficient correlations must exceed 0.3 (Monash University 2005). In order to determine the relative importance of each original variable to the canonical variates, three interpretative approaches are available: (a) canonical weights (standard coefficients), (b) canonical loading (the variable loads on its own variate), and (c) canonical cross-loading (the variable loads on the opposite variate). In this study, the last two approaches were utilized. In addition to using Cronbach s Alpha to test the reliability of the variables, Kappa was used to check the interrater agreement scoring of the TAT from the scores of the two raters. This statistic was used to assess interrater agreement when observing or coding qualitative/ categorical variables. Kappa is considered to be an improvement over using percent agreement to evaluate this type of reliability. Kappa has a range from 0-1.00, with larger values indicating better reliability. Generally, a Kappa >.70 is considered satisfactory. FINDINGS The first canonical correlation function relating two sets of variables (motives and program interests) was calculated and explained 6.3% of the variance in the preference variables. Wilks Lamda was significant for the combined function (Λ=.93156, p<.05). The first canonical correlation indicates how well the groups of motive factors could predict each of the three sets of program interest. Canonical correlation (structured coefficients) (Rc=.243 is the highest possible correlation between any linear combination of the motive variables and any linear combination of the preference variables. As can be seen from a review of data in Table 1, one pair of canonical variates shared significant variation. The structure matrix reveals that high need for affiliation, low need for power, and low need for achievement are predictive of business, science, and hospitality and sport programs., respectively (structure coefficient =-.190, -.647, and -.699, respectively). There was one pair of significant canonical variate (p <.05). Canonical correlations and canonical loadings exceeded 0.3 for the variate. The structure matrix was examined to interpret the variate pair. The pair of variates revealed a very strong relationship between three of the motive variables [need for affiliation (.942), need for power (-.369), and need for achievement (-.302)] and three of the program choice variables (business [-.654], science (.557), and hospitality and sport [-.219]). Essentially, the variate suggests that (1) individuals with a high need for affiliation will not be interested in business programs, (2) individuals with a low need for power will be interested in science program, and (3) individuals with a low need for achievement will not select hospitality and sport programs As a result, there are three conclusions for the test as follows: 1. The need for achievement was significantly correlated with the selection of hospitality and sport program. There is a significant relationship between the need for achievement and the choice of hospitality and sport program. 2. The need for affiliation was significantly correlated with business program. There is a significant relationship between the need for affiliation and the selection of business 425

program. People with a high need for affiliation such as sincerity, glamorous, upper class, good-looking, and charming might not select any business program related to seeking money rather than friendly relationship. 3. The need for power was significantly correlated with the choice of science program. There is a significant relationship between the low need for power and the preference of science program. Students with a low need for power, less reliable, less secure, and less confident might not select any science programs. CONCLUSIONS Psychological need is a critical dimension of students choice for their programs. Universities and colleges should take appropriate strategic action to make their programs more relevant to their students needs. The findings of this study indicated that students with a low need for power, less reliable, less secure, and less confident might be less interested in science programs. Students with a high need for affiliation might not select business programs. Students with a low need for achievement might not study any hospitality and sport programs. The relationships found in the study will help universities and colleges to offer the best programs for their students and strengthen their reputation uniqueness. The application of the psychological dimension to practice will significantly contribute to the gap between the conceptual level and practice of college education in order to help marketers and administrations to better position their reputation. The impact of this study will influence on the sustainability of hospitality and sport programs in specializing their identity for students with high need for achievement. In general, the research shows a clear route to explain the success and failure of the universities and colleges in increasing competition of their reputation. This study featured three contributions that have not been previously examined in the hospitality and sport literature. It demonstrates the exploration of one latent relationship between unconscious needs and choice for college program. This study also contributes to a further understanding of the importance of a great concept of unconscious needs, pioneered by McClelland (1992). Thanks to the achievements of the discovery, the success of this line of research will stimulate future researchers to deepen their knowledge concerning the practical choice for their college based upon their psychological needs. Finally, the study detected three strong relationships: the first one between sciences and need for power, the second one between business and need for affiliation, and finally, the third one between hospitality and need for achievement. Despite the limitation related to the generalization of the sample, the results suggest that awareness of the relationship between unconscious needs and college programs can help faculty to be successful in their competitive strategy in order to meet students demand. Most favorable choice of college program depends on students psychological inventories and the findings of the relationships between identity of college program and students personality. General science curriculums will be studied by people who are not interested in making money. Business program should not offer for people who are working for affiliation organizations or interested in social activities. Finally, hospitality and sport departments should promote their programs to professional people with a high need for achievement. 426

REFERENCES Behrman, J., Kletzer, M., McPherson, M. & Schapiro, O. (1998). Microeconomics of college choice, careers, and wages. The Annals of the American Academy, 559, 12 23. Boudarbat, B. (2006). Field of study choice by community college students in Canada. Economics of Educational Review, 06, 13-44. Card, D. (1999). The causal effects of education on earnings. In Handbook of Labor Economics, O. Ashenfelter and D. Card, editors, Vol 3. Amsterdam: Elsevier Science. Chapman, D.W. (1981). A model of student college choice. The Journal of Higher Education, 52 (5), 490 505. Fuller, W.C., Manski, C.F., & Wise, D.A. (1982). New evidence on the economic determinants of postsecondary schooling choices. Journal of Human Resources,17 (4), 477 498. Hossler, D., Braxton, J. & Coopersmith, G. (1989). Understanding student college choice. In Handbook of theory and research, J.C. Smart, Editor, Vol. 4, pp. 231 288. New York, NY: Agathon Press. Manski, C. F. & Wise, D. A. (1983). College choice in America. Cambridge, MA: Harvard University Press McClelland, D. C. (1992). Motivational Configurations. In Motivation and Personality: Handbook of Thematic Content Analysis, P. Smith, pp. 87-99. New York: Cambridge University Press. McClelland, D. C., Atkinson, J., Clark, R., & Lowell, E. (1953). The Achievement Motive. New York: AppletonCentry-Crofts. McDonough, P. M. (1994). Buying and selling higher education: The social construction of the college applicant. The Journal of Higher Education, 65(4), 427 446. Monash University (2005). Canonical Correlation. Retrieved from the World Wide Web February 7, 2005 <http://www.med.monash.edu.au/psych/research/rda/canonical%20correlation.htm> Murray, H. A. (1943). Thematic Apperception Test Manual. MA: Harvard University Press. Smith, C. P. (Ed., 1992). Motivation and personality: Handbook of thematic content analysis. New York: Cambridge University Press. Soss, N. M. (1974). The demand for college education: The applications decision. Unpublished Ph.D. Dissertation. Princeton, NJ: Princeton University. Weiler, W. C. (1994). Transition from consideration of a college to the decision to apply. Research in Higher Education, 35(6), 631 646. Weinberger, J. & McClelland, D. (1990). Cognitive Versus Traditional Motivational Models. In Handbook of Motivation and Cognition, E. Higgins and R. Sorrentino, 2, pp. 562-597. New York: The Guilford Press. 427

Table 1 Results of the Hypothesis Test Wilk s Λ Eigenvalue R c df Error df F Sig.. 93156 0.063.243 9 659.69 2.16659 <.05 Standardized Coefficients Structure Coefficients Motives N-Achievement -.699 -.302 N-Affiliation -.190.942 N- Power -.647 -.369 College Choice Science -1.381 Business -2.060 Hospitality & Sport -1.929.557 -.654 -.219 428