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Leslie Frassel

Thomas Blashaw

Bethany Sills

Article Summary

Gati, I., Saka, N., Krausz, M. (2001). ‘Should I use a computer-assisted career guidance

system?’ It depends on where your career decision-making difficulties lie. British

Journal of Guidance and Counseling, 29(3), 301-321.

Career decisions are among the most important decisions individuals have to make in their lifetimes. Some individuals have difficulties in making decisive decisions about what career to pursue. Therefore, they use career counseling and/or computer-assisted career guidance systems to facilitate their decision making process in order to ease the difficulty. There are many computer-assisted career guidance systems (CACGSs). They can differ in many respects, such as content, structure, style, and procedure. CACGSs have many advantages. These advantages are: easily updateable information, easy retrieval of information, privacy and anonymity. Yet, can CACGSs serve as a substitute for face-to-face career counseling in dealing with career decision-making difficulties? The current study tries to answer this question. The goal of the current study by Gati, Saka, and Krausz is to assess the pattern of career decision-making difficulties of 417 young adults at different stages of their career decision-making process and to test the usefulness of computer-assisted career guidance systems in reducing these difficulties.

Before we can explain the procedure and methods of this study, you must first understand the three groups that individuals could be placed into. Each participant was put into one of three groups in the PIC model for career decision-making. The first stage of the model is the pre-screening stage, the goal of this stage is to locate a small number of promising option that are well-suited with the individual’s career-related preferences. The second stage is the in-depth exploration stage. The goal of this stage is to collect comprehensive information about each of the promising alternatives and verify their suitability. The third stage is the choice stage. The goal of the third stage is to locate the most suitable alternatives. The experimenters hypothesize that the level of career decision difficulties would be highest for individuals who were at the pre-screening stage, and lowest for individuals who have already reached the choice stage.

The present study was used to examine the patterns of career decision-making difficulties of young adults at different stages of their career decisions (PIC model), and to assess the effectiveness of CACGS-based intervention in reducing these difficulties. Furthermore, the experimenters included taxonomy of career decision-making difficulties. These included: lack of readiness, lack of information, and inconsistent information. The experimenters also hypothesized that they would find positive effects mainly in the four categories of difficulties included under lack of information (Gati, et al.).

The present experiment included 417 participants (153 male and 264 female) who were either soldiers just prior to discharge or recently discharged soldiers. The average ages of the participants ranged from 19-27 and were Caucasian. All participants were facing the transition from high school to work or higher education institutes 3-4 years later than is typical of young adults in many European countries. The first thing that participants had to do was a Career Decision-making Difficulties Questionnaire (CDDQ). The first page of this questionnaire asked the participants to provide general background information: age, sex, and number of years of education. The following three pages included 30 statements, each representing a specific difficulty relating to one of the specific categories of difficulties (Gati, et al.). The participants were asked to rate the degree to which the difficulty represented by each statement on a 9-point scale (1= ‘not severe at all’ 9= ‘describes me well).

The computer-assisted career information and guidance systems used in this study were the MBCD- Making Better Career Decisions. The major goal of this system is to guide its users through the pre-screening stage and help them locate a small set of promising alternatives. This process is done through a sequential elimination model. Also, a Computerized Occupational Information (COI) was used and offered detailed and comprehensive descriptions of over 500 occupations and the required training for relevant occupations as well as information about institutions and schools that provide such training. Another test used was the information about higher education institutions (HEI), which is a system that contains comprehensive information about various majors offered by Israeli universities, and colleges, and it enables the individual to estimate his or her chances of admission to these institutions. Lastly, the participants had to fill out an evaluation questionnaire. This questionnaire was constructed specifically for purposes of the present study, and its main purpose was to collect participants’ subjective evaluations of the CACGSs. Finally, the participants rated the degree to which the systems assisted them on a 5-point-scale (Gati, et al.).

The participants were asked to fill in the CDDQ before accessing the CACGS(s). After the dialogue with the system(s) the participants were asked to complete the evaluation questionnaire and to fill in the CDDQ again. The findings were that no differences were found in the pattern of difficulties experience by the 276 participants who filled in the CDDQ a second time and then who did not. Based on the participants self reports, they were then placed into one of three groups. The first group (N= = 122) consisted of participants who were considered to be past the pre-screening and in-depth exploration stages and were regarded to be at the choice stage. The second group (N = 151) consisted of participants who were considered to be past the pre-screening stage and were regarded to be at the in-depth exploration stage. Lastly, the third group (N = 45) consisted of participants who were considered to be prior to the pre-screening stage.

The following scores were computed for each participant: (a) the scores of the ten CDDQ scales: lack of motivation, indecisiveness, dysfunctional beliefs, the career decision-making process, self, occupations, ways of obtaining information, unreliable information, internal conflicts, and external conflicts (b) the scores of the three major clusters representing the three major difficulty categories: lack of readiness, lack of information, and inconsistent information (c) the total score of the CDDQ. Once this was computed the experimenters used the Pearson product-moment correlation to compute the inter-correlations among the ten CDDQ scales in order to examine the empirical structure of the ten CDDQ scales.

The results were as follows: participants who used COI reported a higher level of having been assisted in receiving information about occupations (mean 3.79), as compared with those who did not use COI (mean 3.17, t(275) = 5.16, p< .01). Also, participants who used HEI reported a higher level of having been assisted in receiving information about universities (mean 3.64) as compared with those who did not use HEI (mean 2.89, t(251) = 5.35, p < .01) (Gati et al.). In order to examine the participants overall perception of usefulness of the CACGSs, the experimenters computed the mean of the five subjective satisfaction variables derived from the evaluation questionnaire, for the various system combinations. The mean level of satisfaction was: (a) higher when all three systems were used (3.54) as compared with the mean level of satisfaction found after the use of only one or two systems (3.24; (t(298) = 3.16, p < .01), and (b) higher when two of the systems were used (3.47) as compared with the mean satisfaction found after using only one system (3.06, t(222) = 3.74, p < 0.01). Finally, the Pearson correlation between the mean ratings of satisfaction and the objective reduction in difficulties, as derived from the comparison of the user’s responses to the CDDQ completed before and after the use of the CACGSs, was 0.30 (p < 0.01) (Gati, et al.).

As anticipated, there were significant quantitative and qualitative differences in the CDDQ scores obtained among participants who were at different stages of their career decision-making processes. Individuals who were characterized as being prior to the pre-screening stage reported the greatest difficulties, and those who were at the choice stage reported the least difficulties. These results demonstrate that the extent of career decision-making difficulties decreases as one progresses through the career decision-making process. As expected, the effect of CACGSs in reducing career decision-making difficulties depended on both the type of difficulty encountered and the particular CACGS(s) used. CACGSs were found to contribute to the reduction of difficulties in seven of the ten career decision-making difficulty categories. The most significant reduction was found in difficulties related to lack of information (d = 0.84) and especially in lack of information about occupations (d = 0.91). The pattern of results indicates that the computer-assistance helped the participants in two ways: (a) by guiding the user through the career decision-making process, and (b) by providing information about occupational alternatives, about the self, and about ways of obtaining additional information. Receiving instruction, guidance, and information about the process apparently helped decrease the participants’ sense of helplessness about making decisions in general, fear of failure, and need to postpone the decision or avoid it (Gati, et al.).

Of course, most all tests have limitations and implications. The present study had some minor implications. These included: (a) the classification of participants into groups corresponded to the three PIC stages was based on their own self reports, (b) the study was carried out in Israel and all participants consisted of young adults who were either soldiers or just prior to discharge. It would be necessary to replicate this study with other samples, outside of Israel, and with other clients.

The present study provided support for the contribution of CACGSs to the reduction of career decision-making difficulties, especially those related to lack of information. Yet, CACGSs should not be regarded as a substitute for career counselors, but rather as a useful addition to the existing counseling tools (Gati, et al.).