Construction and Initial Validation of the Career Maximizing Scale.
Keywords: career decision-making, maximizing, satisficing, decision-making, career assessment
Making optimal career decisions involves seeking out, evaluating, and comparing different options in search of the very best alternative. Decision makers often face a staggering array of university majors, occupations, or jobs to consider, any one of which entails positive and negative aspects (e.g., salary, growth opportunity, and match with personal interests) that must be weighed to approach an optimal decision. The importance and long-term implications of career decisions may, on the one hand, drive some people to maximize their career decisions by carefully weighing options in search of the optimal alternative (Iyengar, Wells, & Schwartz, 2006). On the other hand, career decision makers may be fraught with indecision, stress, and confusion (Germeijs, Verschueren, & Soenens, 2006) that may drive them to take a less onerous, satisficing approach to career decisions.
Understanding how maximizing affects career decision-making is important because career outcomes may partially be a function of how individuals make career decisions. The thought and care that career maximizers put into career decisions may lead to highly satisfying jobs and careers. Conversely, career maximizers' overly constrained range of acceptable careers may impede their ability to commit to a career. The current study aims to add to the existing career decision-making research by (a) presenting and initially validating a self-report measure of career maximizing, (b) examining the relation between domain-general maximizing and career-specific maximizing, and (c) examining the relation between career maximizing and important career outcomes for working adults and university students.
Career decisions refer to choices about "work-related and other relevant experiences, both inside and outside of organizations, that form a unique pattern over the individual's lifespan" (Sullivan & Baruch, 2009, p. 1543). In formulating the career maximizing construct, we included job, career, and occupational decisions. To meet their career goals, individuals must often make various job choices, or within-career decisions (Stevens, 2014). They also may make between-careers choices in the form of changing occupations. Both types of decisions are a part of career maximizing.
One of the most studied variables within the career decision-making literature is indecisiveness, which refers to an individual's inability to make career decisions (Saka, Gati, & Kelly, 2008). Being unable to make effective career decisions is associated with many negative consequences such as lower levels of well-being (Fouad et al., 2006) and lower self-efficacy (Jaensch, Hirschi, & Freund, 2015). Given the increased pressure for people to manage their own careers (Sullivan & Baruch, 2009), the inability to make career decisions can be especially problematic. Although various measures for understanding, assessing, and improving career decisions have been developed (e.g., Harren, 1979; Xu & Tracey, 2015), these measures do not elucidate the extent to which people maximize for their career decisions.
General and Career Maximizing Constructs
A common distinction in the decision-making literature is between maximizing and satisficing (Dalai, Diab, Zhu, & Hwang, 2015). Maximizing involves a systematic evaluation of all alternatives in an effort to make an optimal choice, whereas satisficing involves the selection of a "good enough" choice (Simon, 1956). Some have also included high standards and decision difficulty as components of maximizing (Cheek & Schwartz, 2016; Kim & Miller, 2017; Schwartz et al., 2002). Maximizing is often conceptualized as a traitlike individual differences variable (Diab, Gillespie, & Highhouse, 2008; Schwartz et al., 2002). Accordingly, the term maximizer refers to a person with a general desire to make optimal decisions whereas the term satisficer refers to a person with a desire to make minimally acceptable decisions.
Although existing measures of maximizing examine one or more of the aspects of maximizing that have been identified by various researchers, these measures conceptualize maximizing as a general tendency. The unique status of career decisions (e.g., their long-term impact, importance, stressful nature), however, may cause some people to make career decisions in a way that diverges from their general maximizing tendency (Cheek & Schwartz, 2016). Consequently, the link between general and career-specific maximizing tendencies is unclear and should be examined. Acquiring a better understanding of the role that maximizing plays in career decision-making requires having a domain-specific measure of maximizing. Domain-specific measures such as the one developed in the present study often have greater predictive validity than more generalized measures of the same construct (Shaffer & Postlethwaite, 2012).
Knowledge of career maximizing could have theoretical and practical benefits. A career maximizing measure could be used to examine distinctions between career decisions and decisions in other life domains, helping to reconcile inconsistencies regarding the impact of maximizing on career outcomes. A career maximizing measure could also enhance the career counseling process. Feedback regarding career maximizing tendencies could be quite beneficial for clients and could facilitate improved career management strategies.
Drawing from both the maximizing and career decision-making literatures (e.g., Dalai et al., 2015; Gati, Krausz, & Osipow, 1996), we define career maximizing as the extent to which a person desires to make career decisions in pursuit of maximizing goals (e.g., examining multiple alternatives and not settling for a suboptimal option). Career maximizing thus represents a continuous variable such that career maximizing tendencies exist as a matter of degree rather than being strictly categorical. Because career maximizing can be thought of as a subset of the larger, more general maximizing construct, we anticipate that career maximizing will be related to general maximizing. However, given the unique status of career decisions, people likely make career decisions in a way that somewhat diverges from their general maximizing tendency:
Hypothesis 1: Career maximizing will positively relate to, but be distinct from, general maximizing.
Impact of Maximizing on Career Decisions
Some researchers have reported that general maximizing is associated with undesirable career outcomes. For example, maximizing has been associated with reduced career-choice satisfaction and reduced job and career satisfaction (Dahling & Thompson, 2013; Iyengar et al., 2006). These purported disadvantages of maximizing have prompted some researchers to actually recommend against the use of maximizing when making career decisions (e.g., van Vianen, de Pater, & Preenen, 2009). Other researchers have found, however, that maximizing is associated with many positive cognitions and behaviors that are likely useful in career development, such as a positive future orientation (Zhu, Dalai, & Hwang, 2017). Unfortunately, it is difficult to derive firm conclusions regarding the benefits of maximizing for career decisions from these studies because they did not use career-specific measures of maximizing. These findings are dependent on the tenuous assumption that general and career maximizing are synonymous. Measures of general maximizing that are used to infer career decision-making processes may be deficient given their broad focus (see DeVellis, 2017).
Given that maximizing entails a desire to choose an optimal alternative through considering multiple options, career maximizing should be associated with a willingness to confidently approach career decisions.
That is, people with a desire to select an optimal occupation or job should tend to have greater confidence in their ability to achieve this goal. Accordingly, indicators of confident decision-making (e.g., self-efficacy and ambiguity tolerance; Taylor & Betz, 1983; Xu & Tracey, 2015) are expected to relate to career maximizing:
Hypothesis 2: Career maximizing will positively relate to indicators of career decision-making confidence.
Career maximizing is also expected to relate to a variety of positive affective and attitudinal career outcomes. Because career maximizers seek to select the optimal career, they should be less likely than career satisficers to settle for a job or occupation that they do not desire. Thus, on average, maximizers are expected to put more thought and care into their career decisions. The greater thought and care that goes into career decision-making is expected to be associated with various positive career outcomes, such as career satisfaction:
Hypothesis 3: Career maximizing will positively relate to affective and attitudinal career outcomes.
Finally, compared with generalized maximizing, career maximizing should show stronger relations to career outcomes. As previously mentioned, although these two constructs are expected to be related, they are also anticipated to be distinct. Given that career maximizing is more narrowly contextualized for the career domain than general maximizing, we anticipate that there will be stronger relations between career maximizing and career-related outcomes (e.g., job/career satisfaction) than between general maximizing and career-related outcomes:
Hypothesis 4: Career maximizing will be more strongly related to career-relevant outcomes than general maximizing.
Study 1: Scale Development and Initial Validation of Factor Structure
Method and Materials
To create a measure of career maximizing, we followed standard principles and guidelines of scale development (DeVellis, 2017). We referenced Diab et al.'s (2008) Maximizing Tendency Scale, considered one of the most reliable and valid measures of maximizing, during item generation, building from the Maximizing Tendency Scale's basic item format and including additional contextualization specific to the career domain. The first and second authors, as well as two research assistants, independendy created lists of approximately 10 items that would be reflective of the career maximizing construct. This was done to ensure that the initial pool of items would be substantially larger than the final item list, thereby enabling us to select the optimal items on the basis of subsequent analyses. Having multiple researchers independently generate the items also ensured that the items were a comprehensive representation of the career maximizing construct. Upon completing this standardized procedure, we then aggregated the lists, deleted overly redundant items, and made edits to the remaining items (e.g., reworded ambiguous items) as needed. This resulted in an initial pool of 44 items. The item pool contained a mixture of both career-oriented items and job-oriented items to capture the within-career and between-careers aspects of career decisions. The response format for the career maximizing items (across all three studies) was a Likert scale ranging from 1 = strongly disagree to 5 = strongly agree.
The initial pool of 44 items was administered to a sample of 129 undergraduate students. This sample size is sufficient given the highly favorable ratio of anticipated factors to items (i.e., 1:44) and the level of communality observed (M = .64) among items (see MacCallum, Widaman, Zhang, & Hong, 1999). Of the 129 respondents, nine failed to provide complete data and were therefore not included in the subsequent analyses. Of the remaining participants, 61% identified as men/male, 85% were White/Caucasian, and their mean age was 20 years (SD = 2.57).
Results and Discussion
To determine the appropriate factor structure of the Career Maximizing Scale (CMS), we conducted a series of exploratory factor analyses using the principal-axis factoring technique and oblique (promax) rotation. We compared one-, two-, and three-factor solutions. Given the unidimensionality of the Diab et al. (2008) measure, we tentatively anticipated a one-factor solution. However, it was possible that participants would respond differently to the career-oriented versus job-oriented items or that the measure would exhibit some unanticipated multidimensionality. The results of the exploratory factor analyses indicated that one-, two-, and three-factor solutions explained 27%, 32%, and 36% of the variance, respectively. For the solutions with more than one factor, item loading patterns did not yield meaningful factor content themes. Eigenvalues and the scree plot provided strong evidence for the presence of a dominant primary factor. As such, we retained one factor, which is in concordance with Diab et al.'s conceptualization of maximizing. Given the finding of an essentially unidimensional scale, we then reduced the length of the scale by retaining the eight highest loading career-oriented items and the eight highest loading job-oriented items, along with any remaining items with factor loadings greater than .60. Although the results indicated that the scale was statistically unidimensional, we retained both career-oriented and job-oriented items to ensure adequate construct coverage (content validity). This resulted in a reduced set of 17 items. Factor loadings for the retained items ranged from .51 to .74, and the coefficient alpha was .92.
Study 2: Confirmation of Factor Structure and Establishment of Construct Validity
Method and Materials
In Study 2, we sought to verify the factor structure of the scale, further reduce the length of the scale, and provide initial construct validation of the scale. We administered the reduced 17-item CMS, as well as a variety of other substantive measures, to a sample of working adults. These measures included constructs theoretically relevant to participants' careers (e.g., career satisfaction), jobs (e.g., job satisfaction), and dispositions (e.g., conscientiousness) with the aim of establishing the convergent/discriminant validity of the CMS. Detailed information about these measures can be seen in Table 1.
Two hundred seventy-two working adults recruited from the Amazon Mechanical Turk platform (Buhrmester, Kwang, & Gosling, 2011) participated in this study. To ensure high-quality data, we screened out participants who either failed to provide complete data or engaged in careless responding (e.g., responded incorrectly to at least one statement such as "Please select 'agree' when responding to this item"; Meade & Craig, 2012). The final sample consisted of 227 working adults. Of these 227 working adults, 45% identified as men/male, 74% were White/Caucasian, and the mean age was 34 years (SD = 10.43). Additionally, 75% were employed full-time (25% were employed part-time), and the most common employment industries were education (17.2%) and health (12.8%).
Results and Discussion
To examine the factor structure and psychometric properties of the CMS, we estimated a series of confirmatory factor analysis (CFA) models with maximum likelihood estimation. First, we estimated a model allowing all 17 items to load onto a single career maximizing factor (see Table 2, Model A). On the basis of accepted conventions (e.g., comparative fit index of .95 or greater, root-mean-square error of approximation of .08 or less, standardized root-mean-square residual of .08 or less), the results indicated that this was initially a poor-fitting model. Next, we estimated a second model using the 11 items identified in Study 1 that had factor loadings greater than .65 (see Table 2, Model B). By retaining items with high factor loadings, we are retaining those items that are most strongly related to the career maximizing construct (Brown, 2006). This second CFA yielded improved fit. We also found that a pair of items had content overlap that was causing some misfit in the error covariance matrix. Accordingly, we decided to drop one of these items to eliminate unnecessary redundancy (DeVellis, 2017). We then conducted a third CFA on the finalized set of 10 items (see Table 2, Model C). On the basis of accepted conventions, the finalized set of items had good internal consistency (a coefficient alpha of .88) and adequate fit. The final set of CMS items, as well as the corrected item-total correlations, can be seen in Table 3. We note that we also estimated a two-factor CFA by allowing the job-oriented and career-oriented items to load onto separate factors. The results consistently indicated, however, that a single-factor solution provided a better fit, thereby supporting our initial decision to retain the one-factor solution.
Next, we examined the relations between career maximizing and a variety of other career, job, and dispositional measures (see Table 1). There was a large correlation between general maximizing and career maximizing (r = .68). To examine the degree to which general and career maximizing may be distinct, we conducted two more CFAs. For the first CFA, general and career maximizing items were forced to load onto a single maximizing factor, representing the proposition that general and career maximizing are indistinguishable. For the second model, we allowed items to load onto separate general and career maximizing factors, representing the proposition that career maximizing is distinct from general maximizing. The two-factor solution was a significantly better fit to the data, [DELTA][chi square](1) = 174.06, p < .001, Cohen's w = .88, thereby supporting Hypothesis 1. We also observed that participants tended to exhibit greater career maximizing (M = 3.87) than general maximizing (M = 3.62), paired-samples t(226) = 6.50, p < .001.
As shown in Table 1, career maximizing related positively to various career variables (career satisfaction, career decision-making ambiguity tolerance, career decision-making self-efficacy, career commitment), job variables (job satisfaction, person-organization/value-congruence fit), and dispositional variables (general maximizing, agreeableness, conscientiousness). Furthermore, the results indicated a negative relationship between career maximizing and several dispositional variables (avoidant decision-making, indecisiveness, and neuroticism). Finally, there was no relationship between career maximizing and withdrawal cognitions, person-organization/needs-supplies fit, regret, extraversion, or imagination/intellect. The positive relationship between career maximizing and indicators of confident decision-making (e.g., career decision-making self-efficacy) and career affective variables (e.g., career satisfaction) provides support for Hypotheses 2 and 3. Furthermore, by showing that career maximizing is unrelated to variables that are theoretically irrelevant to the maximizing construct (e.g., extraversion), we provide initial evidence for the discriminant validity of the CMS.
There was less support for Hypothesis 4, regarding career maximizing being more strongly related to career outcomes than general maximizing. Instead, the relations between career maximizing and job satisfaction (r = .18)/career satisfaction (r = .26) were not different from relations between general maximizing and job satisfaction (r = .22)/career satisfaction (r = .25): [Z.sub.job sat] = -.44, p = .65, and [Z.sub.career sat] = .11, p = .91.
Finally, we also examined the relations between the CMS and possible maximizing behaviors relevant to working adults: the number of other jobs they considered when applying to their current job, the number of days they spent deciding whether to accept their current job, and the number of applications they submitted for their previous job. Because these data were positively skewed, we first applied a logarithmic transformation to normalize the data. Consistent with Diab et al. (2008), we then averaged across these three values to form an overall indicator of career maximizing behavior. The results indicated that there was a significant, positive relationship between the CMS and this behavioral indicator of career maximizing (r = .18).
Study 3: Confirmation of Construct Validity and Generalizability of Measure
Method and Materials
In Study 3, we sought to provide further construct validation of the finalized CMS and assess its generalizability to university students. We included additional maximizing scales to clarify the relationship between career maximizing and general maximizing as well as measures specific to academic settings (e.g., major satisfaction; see Table 1). This decision was driven by our desire to further establish the convergent/discriminant validity of the CMS. When completing the CMS, students were asked to indicate how they make/anticipate making career decisions.
Two hundred sixteen undergraduate students participated in this study. As in Study 2, we embedded directed-response questions throughout the survey to assess careless responding. Because participants were expected to complete a greater number of scales, we decided to remove any participants who answered two or more of these questions incorrectly. After we removed these participants as well as those who failed to provide complete data, the final sample consisted of 180 students. Of these 180 students, 38% identified as men/male, 79% were White/ Caucasian, 43% indicated they were employed, and their mean age was 20 years (SD = 2.62).
Results and Discussion
To further the comparison between career maximizing and general maximizing, we included two additional measures of general maximizing (Durinik, Prochazka, & Cigler, 2018; Schwartz et al., 2002). The CMS (a = .84) displayed positive relationships with all three general maximizing measures. All these relationships were significant but, in contrast to the results of Study 2, were moderate in strength ([r.sub.min] = .30 to [r.sub.max] = .38). This result provides further evidence that career and general maximizing are distinct (Hypothesis 1).
Table 1 shows that, overall, the links between career maximizing and career and job constructs were reasonably consistent across samples. The exception may be withdrawal cognitions, which was stronger in students (r = -.31, p < .01) than in working adults (r = .06, p = .42). There were some differences between the samples with respect to how career maximizing related to dispositional constructs (e.g., decision styles, personality). The biggest difference was avoidant decision-making, which was negatively related to career maximizing in adults (r = -.21, p < .01) but positively related to career maximizing in students (r = .18, p < .05).
Three of four academic measures (subjective major fit, career decidedness, and commitment to major; see Table 1) were modestly related to career maximizing, with career decidedness being only marginally significant (r = .15, p = .05). Finally, we examined the relations between the CMS and some possible student-relevant career maximizing behaviors: the number of universities considered, the number of schools that received applications, the number of days deciding which college to attend, and the number of majors considered. Because the data were positively skewed, we applied a logarithmic transformation and then averaged across these four values. Unlike what we found in Study 2, however, in this study we did not find a significant relationship between the CMS and this behavioral indicator of career maximizing among students (r = .03).
Consistent with Study 2, the CMS exhibited significant positive relationships with indicators of confident career decision-making, such as career decision-making ambiguity tolerance (r = .18, p < .05). Career maximizing was also related to desirable, positive career affective and attitudinal outcomes, such as career satisfaction (r = .30, p < .01), although not university major satisfaction (r = .04, p = .59). Taken as a whole, these results offer additional support for Hypotheses 2 and 3.
In contrast to Study 2, none of the three general maximizing measures significantly predicted job satisfaction ([r.sub.mm] = -.20 to [r.sub.max] = -22) or career satisfaction ([r.sub.max] -.03 to [r.sub.max] = .07), whereas career maximizing predicted both job satisfaction (r = .33) and career satisfaction (r = .30). Fisher r-to-Z tests revealed that career maximizing correlated more strongly than general maximizing with job satisfaction when the Ourinik et al. (2018) measure (Z = 3.04, p < .01) or Schwartz et al. (2002) measure (Z = 5.13, p < .001) was used, but not when the Diab et al. (2008) measure was used (Z = 1.12, p = .26). Career maximizing uniformly correlated more strongly than general maximizing with career satisfaction: [Z.sub.Durinik] = 3.19, p < .01; [Z.sub.Schwartz] = 2.25, p = .02; and [Z.sub.Diab] = 2.72, p < .01. At least for certain samples, career maximizing can be a better predictor than general maximizing of job or career outcomes, tentatively supporting Hypothesis 4.
As in Study 2, we compared students' career maximizing with their general maximizing by conducting a series of paired-samples t tests. We found that career maximizing (M = 3.88) was significantly higher than general maximizing when compared using Diab et al.'s (2008) measure (M = 3.78), t(179) = 2.36, p = .019; Schwartz et al.'s (2002) measure (M = 3.36), t(179) = 12.26, p < .001; and Ourinik et al.'s (2018) measure (M = 3.72), t(179) = 3.78, p < .001. People appear to maximize more for their career decisions than they do for their decisions more generally, thereby corroborating the unique status of career decisions.
Across three studies, we developed and initially validated the CMS by administering the measure to student and working adult samples. Career maximizing was distinct from general maximizing, which supports previous assertions of the need for domain-specific conceptualizations and measures of maximizing (e.g., Cheek & Schwartz, 2016). Relative to general maximizing tendencies, participants expressed a greater desire to maximize their career and work decisions, which also confirms the need for a career-specific maximizing measure.
Although some have suggested that maximizing may result in adverse career outcomes (e.g., van Vianen et al., 2009), our results indicate that career maximizing tendencies are associated with many positive career outcomes (e.g., career satisfaction). This was the case in the student and working adult samples. Given the limitation of previous research in relying on general maximizing measures to infer career-specific behaviors, we are inclined to view maximizing as having generally positive career outcomes as described in the current study.
We found mixed support with respect to career maximizing being more predictive than general maximizing of important career and work outcomes. Whereas we found this to be the case for the student sample, the career and general maximizing scales were about equally as effective in predicting career outcomes for working adults. This may be due to the fact that the career and general maximizing constructs were more related for working adults than they were for students.
Our current research provides initial evidence that maximizing can be better understood with a domain-specific measure (Cheek & Schwartz, 2016). Career and general maximizing are distinct constructs. We observed stronger associations between career maximizing and career outcomes in Study 3 and found higher mean levels of career maximizing compared with general maximizing across samples. Although measures of general maximizing are useful, it is unlikely (and arguably impossible) that people maximize for all the decisions they make. This could explain why some researchers have found that measures of general maximizing do not always predict maximizing behavior (e.g., Harman, Weinhardt, & Gonzalez, 2018). Although people have general maximizing tendencies, maximizing behavior may manifest itself only for certain decisions. Thus, domain-specific measures, such as the CMS, may be better suited than general measures to capturing how people make decisions in specific contexts.
Our study also helps to clarify whether there are benefits to maximizing one's career decisions. Although many researchers have noted adverse consequences of maximizing (e.g., Dahling & Thompson, 2013), with few exceptions, we found that career maximizing was associated with positive career and noncareer outcomes. This result aligns with a larger stream of research that has emerged showing that maximizing, although once thought to have rather serious negative consequences, is often associated with positive outcomes and traits (e.g., Diab et al., 2008).
Some people may seek the very best possible job or career, whereas others may be in search of jobs that meet a few very basic requirements. This has important implications that can be leveraged during the career counseling process. First, counselors may want to align their assistance with clients' desires. Maximizers may be in search of the very best achievable job and be willing to wait a long time to reach that goal. They may be highly selective when applying for job/career opportunities and may be willing to turn down imperfect job offers in pursuit of better options. In contrast, satisficers may be in search of jobs that meet only some basic requirements. Presumably, there would be greater latitude in the jobs or career options satisficers would consider. Furthermore, satisficers may prefer to find a minimally acceptable job now rather than wait for something that may or may not be better. Awareness of these preferences can ensure that the career counseling process proceeds in a functional way toward the client's goals.
Second, awareness of career maximizing may be useful in encouraging conversations about career and job decision processes. It is possible that extreme satisficing would lead some people to accept jobs that are not a good fit, which could result in frequent job changes. It is possible that extreme maximizing would lead some people to have an overly constrained range of acceptable jobs. This could result in longer bouts of unemployment or difficulty finding a position deemed acceptable. Conversations about these topics, coupled with use of the CMS, may provide helpful insights that would assist in effective career management.
Finally, use of the CMS with other measures may provide additional insights into clients' career decision-making. For instance, if the Career Decision-Making Difficulties Questionnaire (Gati et al., 1996) revealed that a client has not sought out critical career information, the CMS could discern whether this reflects a broader satisficing approach to career decisions. Or if the Career Decision Scale (Osipow & Winer, 1996) revealed that a client is highly indecisive, the CMS could discern whether this could be due to a strong desire to maximize career decisions. As these examples illustrate, the CMS can provide an enhanced understanding of clients' decision-making goals and processes.
Limitations and Future Directions
Although the present research has many strengths, such as replication across multiple studies, examination of diverse samples, and use of thorough statistical analyses to differentiate career/general maximizing, some limitations should be noted. First, the CMS and other measures used in this research rely on participants self-reporting their thoughts and feelings. As such, we cannot completely rule out the possibility that some participants may have lacked awareness or accurate insights into their career decisions. Although it may be somewhat difficult, it could be beneficial to examine the relations between career maximizing and variables obtained through other means, such as examination of resume data. Second, although it is evident that career and general maximizing are distinct, the exact magnitude of that relation remains somewhat unclear. Our results suggest a stronger general maximizing and career maximizing relationship for adults (Study 2) than for students (Study 3). This could reflect adults having greater alignment between general and career maximizing or could reflect that as people get older, career maximizing increases as work becomes a more central part of people's lives (e.g., Bal & Kooij, 2011). It may also reflect constraints, such as parental influence, that university students contend with when making career decisions. We should note that the CMS does not necessarily capture how contextual factors, such as family pressures, may affect maximizing. Like most measures of individual differences, it is likely that career maximizing is moderated by external factors. Additional work should further explore these possibilities. Finally, future work should further examine how the CMS is related to other measures of career decision-making and continue validating the CMS with different samples (e.g., individuals from other countries, in specific occupations, and at different career stages).
Making optimal career decisions requires consideration of an enormous number of university majors, occupations, or jobs. Whereas some people seek to maximize their career decisions, the stress and difficulty associated with choosing a career may cause others to simply select a career or job that is "good enough." The development of the CMS provides an important step in understanding how people accomplish making decisions about a most consequential aspect of life (i.e., work and career) and the extent to which people desire to make these decisions in pursuit of maximizing goals.
Abdel-Halim, A. A. (1981). A reexamination of ability as a moderator of role perceptions satisfaction relationship. Personnel Psychology, 34, 549-561.
Agho, A. O., Price, J. L., & Mueller, C. W. (1992). Discriminant validity of measures of job satisfaction, positive affectivity and negative affectivity. Journal of Occupational and Organizational Psychology, 65, 185-195.
Bal, P. M., & Kooij, D. (2011). The relations between work centrality, psychological contracts, and job attitudes: The influence of age. European Journal of Work and Organizational Psychology, 20, 497-523.
Blau, G. (1989). Testing the generalizability of a career commitment measure and its impact on employee turnover. Journal of Vocational Behavior, 35, 88-103.
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon's Mechanical Turk: A new source ofinexpensive, yet high-quality, data? Perspectives on Psychological Science, 6, 3-5.
Cable, D. M., & DeRue, D. S. (2002). The convergent and discriminant validity of subjective fit perceptions. Journal of Applied Psychology, 87, 875-884.
Cheek, N. N., & Schwartz, B. (2016). On the meaning and measurement of maximizing. Judgment and Decision Making, 11, 126-146.
Cohen, A. (1997). Nonwork influences on withdrawal cognitions: An empirical examination of an overlooked issue. Human Relations, 50, 1511-1536.
Dahling, J. J., & Thompson, M. N. (2013). Detrimental relations of maximization with academic and career attitudes. Journal of Career Assessment, 21, 278-294.
Dalai, D. K., Diab, D. L., Zhu, X., & Hwang, T. (2015). Understanding the construct of maximizing tendency: A theoretical and empirical evaluation. Journal of Behavioral Decision Making, 28, 437-450.
DeVellis, R F. (2017). Scale development: Theory and application (4th ed.). Los Angeles, CA: Sage.
Diab, D. L., Gillespie, M. A., & Highhouse, S. (2008). Are maximizers really unhappy? The measurement of maximizing tendency. Judgment and Decision Making, 3, 364-370.
Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The mini-IPIP scales: Tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment, 18, 192-203.
Durinik, M., Prochazka, J., & Cigler, H. (2018). The Short Maximization Inventory. Judgment and Decision Making, 13, 123-136.
Fouad, N. A., Guillen, A., Harris-Hodge, E., Henry, C., Novakovic, A., Terry, S., & Kantamneni, N. (2006). Need, awareness, and use of career services for college students. Journal of Career Assessment, 14, 407-420.
Gati, I., Krausz, M., & Osipow, S. H. (1996). A taxonomy of difficulties in career decision making. Journal of Counseling Psychology, 43, 510-526.
Germeijs, V., & De Boeck, P. (2002). A measurement scale for indecisiveness and its relationship to career indecision and other types of indecision. European Journal of Psychological Assessment, 18, 113-122.
Germeijs, V., Verschueren, K., & Soenens, B. (2006). Indecisiveness and high school students' career decision-making process: Longitudinal associations and the mediational role of anxiety. Journal of Counseling Psychology, 53, 397-410.
Greenhaus, J. H., Parasuraman, A., & Wormley, W. M. (1990). Effects of race on organizational experiences, job performance evaluations, and career outcomes. Academy of Management Journal, 33, 64-86.
Harman, J. L., Weinhardt, J. M., & Gonzalez, C. (2018). Maximizing scales do not reliably predict maximizing behavior in decisions from experience. Journal of Behavioral Decision Making, 31, 402-414.
Harren, V. A. (1979). A model of career decision-making for college students. Journal of Vocational Behavior, 14, 119-133.
Iyengar, S. S., Wells, R. E., & Schwartz, B. (2006). Doing better but feeling worse: Looking for the "best" job undermines satisfaction. Psychological Science, 17, 143-150.
Jaensch, V. K., Hirschi, A., & Freund, P. A. (2015). Persistent career indecision over time: Links with personality, barriers, self-efficacy, and life satisfaction. Journal of Vocational Behavior, 91, 122-133.
Kim, K., & Miller, E. (2017). Vulnerable maximizers: The role of decision difficulty. Judgment and Decision Making, 12, 516-526.
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4, 84-99.
Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17, 437-455.
Meyer, J. P., Allen, N. J., & Smith, C. A. (1993). Commitment to organizations and occupations: Extension and test of a three-component conceptualization. Journal of Applied Psychology, 78, 538-551.
Nauta, M. M. (2007). Assessing college students' satisfaction with their academic majors. Journal of Career Assessment, 15, 446-462.
Osipow, S. H., & Winer, J. L. (1996). The use of the Career Decision Scale in career assessment. Journal of Career Assessment, 4, 117-130.
Prevatt, F., Li, H., Welles, T., Festa-Dreher, D., Yelland, S., & Lee, J. (2011). The Academic Success Inventory for College Students: Scale development and practical implications for use with students. Journal of College Admission, 211, 26-31.
Saka, N., Gati, I., & Kelly, K. R. (2008). Emotional and personality-related aspects of career decision-making difficulties. Journal of Career Assessment, 16, 403-424.
Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83, 1178-1197.
Scott, S. G., & Bruce, R. A. (1995). Decision-making style: The development and assessment of a new measure. Educational and Psychological Measurement, 55, 818-831.
Shaffer, J. A., & Postlethwaite, B. E. (2012). A matter of context: A meta analytic investigation of the relative validity of contextualized and noncontextualized personality measures. Personnel Psychology, 65, 445-494.
Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63, 129-138.
Stevens, C. K. (2014). A decade of job choice research. In S. Highhouse, R. Dalai, & E. Salas (Eds.), Judgment and decision making at work (pp. 102-120). New York, NY: Routledge.
Sullivan, S. E., & Baruch, Y. (2009). Advances in career theory and research: A critical review and agenda for future exploration. Journal of Management, 35, 1542-1571.
Taylor, K. M., & Betz, N. E. (1983). Applications of self-efficacy theory to the understanding and treatment of career indecision. Journal of Vocational Behavior, 22, 63-81.
van Vianen, A. E. M., de Pater, I. E., & Preenen, P. T. Y. (2009). Adaptable careers: Maximizing less and exploring more. The Career Development Quarterly, 57, 298-309. doi: 10.1002/j.2161-0045.2009.tb00115.x
Xu, H., & Tracey, T. J. (2015). Career Decision Ambiguity Tolerance Scale: Construction and initial validations. Journal of Vocational Behavior, 88, 1-9.
Zhu, X., Dalai, D. K., & Hwang, T. (2017). Is maximizing a bad thing? Journal of Individual Differences, 2, 94-101.
Nathaniel M. Voss, Christopher J. Lake, and Cassandra Chlevin-Thiele, Department of Psychological Sciences, Kansas State University. The authors thank Taylor Hofeling and Morgan Griffs for their help with creating items for the Career Maximizing Scale and putting together the surveys. Correspondence concerning this article should be addressed to Nathaniel M. Voss, Department of Psychological Sciences, Kansas State University, 562 Bluemont Hall, 1114 Mid-Campus Drive North, Manhattan, KS 66506 (email: firstname.lastname@example.org).
TABLE 1 Study Variables, Correlations With the Career Maximizing Scale, and Citations for Studies 2 and 3 Construct Study 2 r Study 3 r Career constructs Career satisfaction .26 ** .30 ** Career decision-making .34 ** .18 * ambiguity tolerance Career decision-making .41 ** .32 ** self-efficacy Career commitment .19 ** .28 ** Job constructs Job satisfaction .18 ** .33 ** Withdrawal cognitions .06 -.31 ** Person-organization fit .14 * .27 * (value congruence) Person-organization fit .10 .23 * (needs-supplies) Dispositional constructs Maximizing (Maximizing .68 ** .36 ** Tendency Scale) Maximizing (Short -- .30 ** Maximizing Inventory) Maximizing (Maximizing Scale) -- .38 ** Avoidant decision-making -.21 ** .18 * Indecisiveness -.23 ** .01 Regret -.13 .20 ** Extraversion .11 .02 Neuroticism -.16 * -.03 Agreeableness .22 ** .07 Conscientiousness .16 * .22 ** Imagination/intellect .09 -.04 Academic constructs Major satisfaction -- .04 Subjective major fit -- .18 * Career decidedness -- .15 Commitment to major .16 * Construct Citation Career constructs Career satisfaction Greenhaus et al. (1990) Career decision-making Xu & Tracey (2015) ambiguity tolerance Career decision-making Taylor & Betz (1983) self-efficacy Career commitment Blau (1989) Job constructs Job satisfaction Agho et al. (1992) Withdrawal cognitions Cohen (1997) Person-organization fit Cable & DeRue (2002) (value congruence) Person-organization fit Cable & DeRue (2002) (needs-supplies) Dispositional constructs Maximizing (Maximizing Diab et al. (2008) Tendency Scale) Maximizing (Short Durinik et al. (2018) Maximizing Inventory) Maximizing (Maximizing Scale) Schwartz et al. (2002) Avoidant decision-making Scott & Bruce (1995) Indecisiveness Germeijs & De Boeck (2002) Regret Schwartz et al. (2002) Extraversion Donnellan et al. (2006) Neuroticism Donnellan et al. (2006) Agreeableness Donnellan et al. (2006) Conscientiousness Donnellan et al. (2006) Imagination/intellect Donnellan et al. (2006) Academic constructs Major satisfaction Nauta (2007) Subjective major fit Abdel-Halim (1981) Career decidedness Prevatt et al. (2011) Commitment to major Meyer et al. (1993) Note. Coefficient alphas across all measures ranged from .71 to .94 in Study 2 and from .63 to .93 in Study 3. In Study 3, only students who indicated they had a job were permitted to complete the job measures. Also, only students who indicated that they had begun thinking about their career or selected their major were permitted to complete the career measures and academic measures, respectively. * p < .05. ** p < .01. TABLE 2 Summary of Confirmatory Factor Analysis Results, Model Comparisons, and Psychometric Properties for the Career Maximizing Scale From Study 2 Model [chi square] df CFI RMSEA 90% CI SRMR Model A (a) 533.43 *** 119 .80 .13 [12, .14] .07 Model B (b) 127.20 *** 44 .92 .09 [.07, .11] .06 Model C (c) 78.42 *** 35 .95 .08 [.05, .10] .05 Model AIC [alpha] Model A (a) 8,990.91 .93 Model B (b) 5,872.45 .89 Model C (c) 5,292.14 .88 Note. CFI = comparative fit index; RMSEA = root-mean-square error of approximation; CI = confidence interval; SRMR = standardized root-mean-square residual; AIC = Akaike's information criterion. (a) One factor with all 17 items. (b) One factor with 11 items that had factor loadings greater than .65. (c) One factor with final 10-item scale. *** p < .001. TABLE 3 Final Career Maximizing Scale Items and Corrected Item-Total Correlations for Studies 2 and 3 Item-Total Correlation Item Study 2 Study 3 1. I have to know all of the jobs available to .54 .52 me before I can choose a job. 2. I choose jobs that will maximize my career. .62 .56 3. I would hate settling for a job that Is not .59 .52 the best option. 4. When thinking about my career choices, I .55 .57 try to imagine what all the possibilities are. 5. I look at many job options before choosing .62 .55 a job. 6. I carefully weigh the pros and cons of a .55 .53 job before accepting it. 7. When choosing a career, I always try to .71 .54 choose the best one. 8. I am constantly trying to find the very .66 .55 best job. 9. When it comes to careers, I have a very .62 .51 high standard for myself. 10. It's important that I find the very best .73 .57 place to work. Note. Responses were provided on a Likert scale ranging from 1 = strongly disagree to 5 = strongly agree.
|Printer friendly Cite/link Email Feedback|
|Author:||Voss, Nathaniel M.; Lake, Christopher J.; Chlevin-Thiele, Cassandra|
|Publication:||Career Development Quarterly|
|Date:||Jun 1, 2019|
|Previous Article:||Predicting STEM Major and Career Intentions With the Theory of Planned Behavior.|
|Next Article:||Situational Interest and Scientific Self-Efficacy: Influence of an Energy Science Career Intervention.|