Development and Initial Validation of the Constructivist Beliefs in the Career Decision-Making Scale

Abstract

Although career adaptability and constructivist beliefs both capture important aspects of career construction, previous research has predominantly focused on career adaptability and ignored the importance of constructivist beliefs. Drawing on career construction theory and decision-making science, the current study proposes two factors (i.e., satisficing decision and agentic creation) of constructivist beliefs in career decision-making (CBCD) and develops and initially validates a scale measuring the two factors. Study 1 develops the CBCD Scale and supports the two-factor structure of the CBCD through exploratory factor analysis. Study 2 supports the internal consistency reliability of the CBCD and cross-validates the two-factor structure of the CBCD through confirmatory factor analysis. Additionally, Study 2 finds support for the incremental predictions of the CBCD for career indecision and career decision ambiguity management over and beyond career adaptability. The theoretical and practical implications of the CBCD are discussed, as are the limitations and suggestions for future research.

Keywords

career construction constructivist beliefs career decision-making psychometric performance measurement

While person–environment (P-E) correspondence models such as Holland’s (1997) interest typology offer an effective and systematic tool based on individual differences for career decision-making (Nauta, 2010; Tracey & Robbins, 2006), Savickas and his colleagues noted a complementary need to emphasize clients’ self-agency and authorship in career decision-making (Savickas, 2013; Savickas et al., 2009). Thus, they proposed career construction theory to emphasize an agentic and subjective construction process in career decision-making and career development (Savickas, 2013; Savickas et al., 2009). To examine the validity of career construction theory in career decision-making, one critical area is to examine how career construction influences the decision-making process and outcomes. However, while previous research regarding career construction predominantly focused on career adaptability (i.e., the self-regulatory capacities needed for construction; Ginevra, Pallini, Vecchio, Nota, & Soresi, 2016; Hirschi, Herrmann, & Keller, 2015; Rudolph, Lavigne, & Zacher, 2017), there has been little research exploring another important construction-related construct in career decision-making: constructivist beliefs. Constructivist beliefs, which are defined as beliefs about the constructivist nature of the goal of career decision-making and the reality of a good career choice, determine the extent to which one’s expectations regarding career decision-making/counseling align with contemporary career decision-making and career construction theory (Savickas, 2013; Savickas et al., 2009), and are thus expected to predict the process and outcomes of career decision-making and construction-oriented career counseling. To facilitate research and counseling about career decision-making from this perspective of career construction, the purpose of the present study was to develop and initially validate a scale measuring one’s constructivist beliefs in career decision-making (CBCD).

Constructivism in Career Decision-Making

Career construction theory and related research (e.g., Ginevra et al., 2016; Hirschi et al., 2015; Rudolph et al., 2017) have a foundation in personal and social constructivism (Savickas, 1995, 2013; Savickas et al., 2009). As a philosophy of science, constructivism (including both personal and social constructivism) entails the validity of multiple subjective realities and the process of constructing internal truth; in contrast, positivism emphasizes the validity of a single objective reality and the process of discovering external truth (Fosnot, 2013; Kukla, 2013). In other words, constructivism essentially holds that there is no single objective truth waiting to be discovered; rather, individuals construct their mental schema to make sense of observed social and psychological phenomena (Fosnot, 2013; Raskin, 2002). Since the 20th century, constructivism has increasingly influenced many social science disciplines including education, sociology, and psychology (Fosnot, 2013; Raskin, 2002; Von Glasersfeld, 2012). While constructivism has been influential in psychology in learning and cognitive development (Fosnot, 2013; Fosnot & Perry, 1996; Raskin, 2002), it has become a growing force in vocational psychology, particularly over the past 20 years (Savickas, 2013; Savickas et al., 2009). However, constructivism has been rarely applied in career decision-making.

However, one could argue that constructivism is likely to align with contemporary career decision-making because of their shared emphasis on uncertainty (Fosnot, 2013; Savickas, 2013). Although Holland’s (1997) model continues to have tremendous effectiveness in helping individuals make career decisions (Nauta, 2010; Nye, Su, Rounds, & Drasgow, 2017; Tracey & Robbins, 2006), some of its implicit premises may not align with the age of information technology and big data such as the stability of individual and environmental characteristics and the predictability of career development (Savickas et al., 2009; Xu & Tracey, 2017c). Scholars have argued that the dynamic and ever-changing nature of contemporary career development requires an appreciation and acknowledgment of uncertainty in contemporary career decision-making (Krumboltz, 2009; Pryor & Bright, 2007; Xu & Tracey, 2014). More specifically, one’s career trajectory could be easily influenced, diverted, and shaped by unpredictable individual and environmental factors, rendering one’s career prospects uncertain and subject to one’s own subjective construction process. Therefore, Savickas and his colleagues proposed career construction theory, emphasizing a person’s architect role in designing and directing his or her career (Savickas, 2013; Savickas et al., 2009).

Career construction theory advocates an individual’s career construction process and constructor role in the theory’s emphasis on career adaptation and adaptability (Savickas, 1995, 2013; Savickas et al., 2009). Savickas and Porfeli (2012) defined career adaptability as the psychological resources required to cope with adaptation when individuals are facing developmental tasks, vocational transitions, and work traumas. Extensive research has proliferated in recent years to examine the role of career adaptability in career development and career decision-making. For example, Rudolph, Lavigne, and Zacher (2017) meta-analytically revealed associations between career adaptability and a variety of satisfaction and well-being variables. They also revealed a link between career adaptability and career decision-making self-efficacy. Xu and Tracey (2015) directly examined the association between career adaptability and an important decision-making outcome, career indecision. It was found that career adaptability was negatively associated with career indecision, indicating that individuals with more adaptation resources are likely to have less career indecision. The revealed utility of career adaptability in facilitating one’s career decision-making provides initial support for the validity of career construction theory in career decision-making. However, we argue that career adaptability primarily taps into capacities required to initiate and complete construction, and an examination of the importance of career construction is incomplete without an investigation of the role of CBCD.

CBCD

Constructivist beliefs represent an important construct that is different from career adaptability in career decision-making because they denote whether one conceptualizes career decision-making/development as a construction process in terms of the goal of decision-making and the reality of a good choice, whereas career adaptability concerns whether one has the psychological strength to achieve good career construction. In other words, while career adaptability taps into the capability of career construction, constructivist beliefs tap into openness to career construction. For example, an individual who has a great capacity for career adaptation might still believe that the purpose of career decision-making is to discover the predetermined best career choice. If so, this individual may not benefit from construction-oriented career counseling (because of a mismatch in terms of the expectation of the change process) and still have difficulty in adjusting to contemporary career decision-making (because of a preoccupation with an objective optimal choice; Savickas et al., 2009; Xu & Tracey, 2017c). Therefore, we argue that career adaptability and constructivist beliefs represent two distinct and complementary constructs in career construction that predict the process and outcomes of contemporary career decision-making.

Based on career construction and related theories (e.g., Krumboltz, 2009; Savickas, 2013) and decision-making (particularly economic decision-making) science focused on satisficing and dynamic decision-making (e.g., Hastie & Dawes, 2010; Simon, 1972), we propose two important factors corresponding to the two areas of constructivist beliefs (i.e., the goal of decision-making and the reality of a good choice) that are related to career decision-making: the satisficing decision and agentic creation. The satisficing decision factor is derived from Simon’s (1972) theory of bounded rationality, which describes an adaptive decision-making approach (i.e., satisficing decision-making) aimed at making a good enough choice (rather than the optimal choice). Therefore, the satisficing decision factor denotes one’s belief regarding whether there is an optimal career choice and whether people should look for a good enough career choice. Satisficing decision-making is important for the process and outcomes of career decision-making because by lowering the threshold of choice goodness, it could reduce the effort needed for information calculation and increase the satisfaction with a career choice (Brown et al., 2012; Schwartz et al., 2002).

However, is satisficing decision-making adaptive for career decision-making? We argue that it is so because it is cost ineffective, if not impossible, to estimate the future of a career path, compare multiple options, and then identify an optimal choice in the fluid and complex context of contemporary career decision-making (Savickas et al., 2009; Xu & Tracey, 2017c). Research has found that a tendency to look for an optimal choice was negatively associated with major satisfaction, perceived competence, and grade point average (Leach & Patall, 2013) and positively associated with perfectionism and regret (Schwartz et al., 2002). Thus, it is plausible that satisficing decision-making can help people reduce perfectionism and increase efficiency in career decision-making.

In addition to the satisficing decision, we propose agentic creation as another important factor of constructivist beliefs. This factor concerns the reality of a good career choice and denotes a belief that the goodness of a career choice is open to change and depends on choice implementation. Agentic decision-making is important for the process and outcomes of career decision-making because it can reduce fear of a “wrong” choice and increase agency in career adjustment/adaptation. Different endorsement of agentic creation likely leads to different decision-making patterns. If a decision-maker believes in agentic construction of a good career path, focusing on choice implementation and adjustment appears reasonable. In contrast, if a decision-maker holds that a career choice leads only to a fixed outcome (e.g., success, satisfaction, or turnover) and that a choice-implementation process only serves to discover this predetermined outcome, investing extensive resources and energy in finding a good career choice appears inevitable.

However, research has demonstrated that real-life decision-making (including career decision-making) is often dynamic decision-making, rending an agentic perspective of decision-making adaptive (Brehmer, 1992; Gonzalez, 2005). Brehmer (1992) described dynamic decision-making as a decision-making situation in which a series of decisions are required, the decisions are not independent of each other, and the decision-making context changes as a consequence of the decision-maker’s actions. Career decision-making clearly demonstrates the interactive and dialectical features of dynamic decision-making. For example, an originally bad choice could turn out to be an inspiration for one’s career, as one might find one’s true career passion through active learning from the struggle. Therefore, career construction theory (Savickas, 2013; Savickas et al., 2009) emphasizes that a good career is not unfolded but rather constructed. Similarly, happenstance learning theory (Krumboltz, 2009) emphasizes the importance of learning from predicted and unpredicted life events and using the learning experiences to guide future career decisions. Therefore, we argue that agentic creation acknowledges the interactive and constructivist features of contemporary career development and is thus adaptive.

Constructivist Beliefs With Ambiguity Management and Career Indecision

While the two factors of constructivist beliefs generally have a positive role in career decision-making, we further specifically propose that they could have important influences on two key areas of career decision-making: career decision ambiguity management and career indecision. Research has suggested that how individuals address inevitable ambiguity in career decision-making is likely a key process affecting the outcome of career decision-making (e.g., Storme, Celik, & Myszkowski, 2017; Xu & Tracey, 2014, 2015, 2017b). Xu and Tracey (2015) termed an individual’s evaluation of and reaction to ambiguity in career decision-making as career decision ambiguity management and in a U.S. sample, observed that career decision ambiguity management predicted career decision-making self-efficacy, career indecision, and career adaptability beyond general ambiguity tolerance. The importance of career decision ambiguity management has also been documented in cross-cultural research (Storme et al., 2017; Xu, Hou, Tracey, & Zhang, 2016) and longitudinal (Xu & Tracey, 2017c) research in the United States. Among the four factors of career decision ambiguity management (i.e., preference, tolerance, confidence, and aversion), research has generally portrayed the first three as adaptive reactions to ambiguity and the last one as a maladaptive reaction to ambiguity (Xu et al., 2016; Xu & Tracey, 2015, 2017a).

We anticipate that both factors of constructivist beliefs would lead to positive ambiguity management because constructivist beliefs could help individuals experience ambiguity in career decision-making as a more interesting, acceptable, and manageable feature (i.e., preference, tolerance, and confidence) and consequently feel less fearful about potential mistakes resulting from ambiguity. In contrast, ambiguity is likely to appear to be a threat to individuals who believe that there is an optimal career choice and that they can only search to discover it. Because of their elevated fear of mistakes, these individuals are expected to exhibit a maladaptive reaction to ambiguity (i.e., aversion).

In addition to career decision ambiguity management, career indecision is another important area for assessing the role of CBCD. Career indecision refers to difficulties contributing to a state of being undecided about one’s educational or occupational path. Given its direct relevance to the outcome of career decision-making and its high prevalence as a presenting concern in career counseling (Gati, Krausz, & Osipow, 1996; Osipow, 1999), career indecision has been a central topic of vocational psychology and career counseling for nearly a century (Osipow, 1999; Xu & Bhang, in press). Research has also repeatedly supported the associations between career indecision and major/academic satisfaction (Jadidian & Duffy, 2012; Komarraju, Swanson, & Nadler, 2014) and well-being (Saunders, Peterson, Sampson, & Reardon, 2000; Walker & Peterson, 2012). Xu and Bhang (in press) summarized previous research and found that a four-factor structure (Brown et al., 2012) comprehensively and parsimoniously represents indecision factors in Western contexts: neuroticism/negative affectivity, choice/commitment anxiety, lack of readiness, and interpersonal conflicts.

We anticipate that both factors of constructivist beliefs will lead to fewer difficulties in all four indecision domains. When people believe that searching for the best choice is unnecessary and the goodness of a career choice depends on choice execution, they are more likely to tolerate ambiguity and potential mistakes in career decision-making. Consequently, they tend to experience less pressure and fewer difficulties in initiating career decision-making (i.e., lack of readiness), committing to a single choice (i.e., choice/commitment anxiety), and regulating interpersonal disagreement (i.e., interpersonal conflicts). Similarly, because both factors of constructivist beliefs could help reduce the cognitive burden of decision-making, people with these beliefs are anticipated to regulate their negative emotions (i.e., neuroticism/negative affectivity) better.

Although constructivist beliefs could supplement career adaptability in predicting important processes and outcomes of career decision-making, little research has been conducted in this area, likely due to a paucity of psychometrically sound measures for this construct. There have been two scales developed to measure more broad beliefs/thoughts related to career decision-making: the Career Beliefs Inventory (CBI;(Krumboltz, 1994) and the Career Thoughts Inventory (CTI;(Sampson, Peterson, Lenz, Reardon, & Saunders, 1998). However, neither of these sufficiently and directly measures beliefs about satisficing and agentic decision-making because the CBI lacks consistent psychometric support for its structure and the CTI mainly addresses thoughts reflecting (not precipitating) career decision-making difficulties (Kleiman et al., 2004; Walsh, Thompson, & Kapes, 1997). We thus developed and initially validated the CBCD Scale in the present study to not only facilitate future research about constructivist beliefs but also help counselors assess their clients’ constructivist beliefs during career counseling.

Summary of the Present Study

Although career construction has been emphasized in the theoretical and practical endeavor of career counseling (Busacca & Rehfuss, 2016; Savickas, 1995, 2013), little is known about the important role of CBCD. To facilitate research and counseling about constructivist beliefs, the purpose of this study was to develop and initially validate a measure of CBCD. In Study 1, we constructed the CBCD to measure the two factors of constructivist beliefs and initially examined the factorial structure of the CBCD. In Study 2, we further examined the structural validity of the CBCD and its incremental validity in predicting career decision ambiguity management and career indecision over and beyond career adaptability.

Study 1

In Study 1, the initial CBCD was constructed to measure a person’s belief satisficing decision and agentic creation.

Sample

The analysis used a diverse sample comprising 223 individuals ranging in age from 18 to 25 (M = 23.82, SD = 1.65). Of the sample, 34.5% were female (n = 77), 64.1% were male (n = 143), and 1.3% (n = 3) did not identify as either female or male. In terms of race/ethnicity, 8.5% (n = 19) were African American/Black, 33.2% (n = 74) were Asian/Asian American, 5.8% (n = 13) were Latino(a)/Hispanic, 45.7% (n = 102) were Caucasian/White, 4.0% (n = 9) were Native American, and 0.9% (n = 2) were multiracial. In terms of socioeconomic status (SES), 4.9% (n = 11) identified as lower class, 21.1% (n = 47) identified as working class, 61.4% (n = 137) identified as middle class, 11.7% (n = 26) identified as upper middle class, and 0.9% (n = 2) identified as upper class. Of the sample, 55.2% (n = 123) identified as a student and 44.8% (n = 100) identified as a nonstudent.

Measurement

We followed the suggestions of Worthington and Whittaker (2006) to guide our process of item development. First, along with conducting a review of the literature about career decision-making, we held research meetings to brainstorm and hold discussions regarding the development of the initial items (e.g., “It is unnecessary to search for the best career choice” and “The consequence of a career choice varies by how one executes the choice”). Second, we contacted two career experts for feedback regarding the content validity and clarity of items. The two external experts were one licensed psychologist supervising a career service center at a large state university and one counseling psychology faculty member who was certified by the American Board of Professional Psychology. Third, based on the feedback of the experts, we revised the items to improve their clarity and the conceptual representativeness of CBCD (e.g., we replaced the vague term “consequence” with the more concrete term “success”). The final version of the initial item pool comprised 16 items, with 8 items each for the Satisficing Decision and Agentic Creation subscales. The CBCD instructed participants to rate the items on a 7-point Likert-type scale, ranging from 1 (strongly disagree) to 7 (strongly agree). For each subscale, higher scores indicated stronger endorsement.

Procedure

Participants were recruited through Amazon Mechanical Turk (MTurk). The age range of the sample aligns with the age of traditional college students and thus could represent career decision-making experiences in populations that are traditionally deemed active in career decision-making (Lipshits-Braziler, Gati, & Tatar, 2016; Osipow, 1999). Amazon MTurk has rapidly become a popular means of data collection across social sciences in recent years. It offers affordable access to diverse populations, and its validity in collecting high-quality data has been demonstrated to be equivalent to or even better than traditional methods such as recruiting college students (e.g.,(Buhrmester, Kwang, & Gosling, 2011; Paolacci & Chandler, 2014; Ramsey, Thompson, McKenzie, & Rosenbaum, 2016).

MTurk workers were invited to participate voluntarily in this study. They first reviewed the human intelligence task (HIT) created for this study and decided whether they wanted to accept the HIT. Once they consented to participate, they accepted the HIT and completed a demographic questionnaire and the initial CBCD online. They were compensated (US$0.5, which is a common and accepted rate on MTurk for the anticipated amount of time) for their valid participation through the MTurk reward system. All responses remained anonymous and confidential throughout the analysis. To further strengthen data validity, we retained 223 of the 250 people who submitted the HIT and properly followed the instructions for the validity screening items (e.g., please choose “3”). The final data set did not find missing data for analysis variables. Institutional review board approval was obtained for the study.

Analysis

We conducted exploratory factor analysis (EFA) to explore the factor structure and good items of the CBCD. In Study 1, we used principal axis factoring to extract factors and conducted a parallel analysis with 1,000 replications and 95% as the cutoff to cross-validate the initial decision about the factor number. We then used direct Oblimin rotation to derive the interpretable factor pattern and accordingly named factors (Kahn, 2006; Worthington & Whittaker, 2006). In the last step of the EFA, to obtain a simple measurement structure and improve the measurement efficiency, we retained the best items of each factor, defined as those with the greatest factor loadings (at least .50), minimal cross-loadings (less than .30), and comprehensive content representation (Kahn, 2006; Worthington & Whittaker, 2006).

Results

There were several prerequisites for the formal procedure of EFA. We first examined the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity to ensure the adequacy of the current data for factor analysis (Kahn, 2006; Worthington & Whittaker, 2006). The current sample revealed a KMO value of .86 and a significant Bartlett’s test (p < .05), indicating adequate covariance among variables for factor analysis. Additionally, the current sample revealed medium item communalities (most ranged from .4 to .6) and high factor overdetermination (i.e., eight indicators per factor and two factors in total); therefore, the current sample size (>200) was adequate for EFA (MacCallum, Widaman, Zhang, & Hong, 1999).

In EFA, the results of principal axis factoring suggested a two-factor structure. We found that both factors had eigenvalues >2.0, accounting for a cumulative 44.07% of the total variance. In addition, a parallel analysis based on random data sets also suggested a two-factor structural model. Therefore, we adopted a two-factor structure as the best model. Subsequently, we reviewed items that loaded on each rotated factor and found that all items loaded on targeted factors except 2 items of satisficing decision. However, the two factors still represented the intended two conceptual factors: satisficing decision and agentic creation.

Finally, based on the criteria of strong factor loadings, minimal cross-loadings, and content representativeness, we selected the best items for each factor. Because the factor of satisficing decision had 6 items that met the criteria, we retained these 6 items for satisficing decision. The 6 items all exhibited major loadings of greater than .60 and cross-loadings less than .25. For the factor of agentic creation, we also retained 6 items, all of which exhibited major loadings greater than .59 and cross-loadings less than .20. Table 1 lists the items for the final version of the CBCD. As shown, the two factors conceptually represented satisficing decision and agentic creation, respectively. The factor pattern also demonstrated a desirable simple measurement structure. Therefore, the psychometric evidence of EFA provided initial support for the structural validity of the CBCD and warranted more stringent structural examination of the CBCD in confirmatory factor analysis (CFA).

Table 1.

Factor Loading for Exploratory Factor Analysis.

Items	Factor I	Factor II
It is unnecessary to search for the best career choice	0.79	−0.09
One should look for “good enough” career choices	0.78	0.17
The process of career decision-making can stop when one finds several “good enough” choices	0.72	0.08
There is no optimal career choice	0.71	−0.22
One should not try to look for an optimal choice	0.66	0.12
The goal of career decision-making is to find a “good enough” career	0.61	−0.03
Career success relies on good execution and adjustment of one’s career choice	−0.05	0.70
How one implements a chosen career determines one’s career success	−0.01	0.68
The goal of career decision-making is to initiate the building process of one’s career	−0.05	0.67
One needs to collect information to understand the potential of several promising careers	−0.08	0.67
People need to work hard after making a career choice to make their career choice work	−0.15	0.66
The implementation of one’s career choice determines one’s career success	0.19	0.59

Note. Factor I = constructivist beliefs in career decision-making-satisficing decision; Factor II = constructivist beliefs in career decision-making-agentic creation.

Values in bold are significant at .05.

Study 2

In Study 2, we used CFA to further examine the structural validity of the CBCD in a separate sample. Additionally, we examined the incremental validity of the CBCD in predicting career decision ambiguity management and career indecision over and beyond career adaptability.

Sample

The current diverse sample comprised 292 individuals from the United States ranging in age from 18 to 25 (M = 23.51, SD = 1.93). Of the sample, 63.0% were female (n = 184), 36.6% were male (n = 107), and 0.3% (n = 1) did not identify as either female or male. In terms of race/ethnicity, 14.7% (n = 43) were African American/Black, 10.3% (n = 30) were Asian/Asian American, 8.6% (n = 25) were Latino(a)/Hispanic, 62.7% (n = 183) were Caucasian/White, 1.4% (n = 4) were Native American, and 2.4% (n = 7) were multiracial. In terms of SES, 8.9% (n = 26) identified as lower class, 38.7% (n = 113) identified as working class, 44.2% (n = 129) identified as middle class, and 8.2% (n = 24) identified as upper middle class. Of the sample, 51.0% (n = 149) identified as a student and 49.0% (n = 143) identified as a nonstudent.

Measurement

CBCD Scale

The 12-item CBCD resulting from Study 1 was administered to measure an individual’s CBCD. There are two subscales of the CBCD: Satisficing Decision (6 items) and Agentic Creation (6 items). The subscale of Satisficing Decision measures the extent to which an individual believes that there is no best career choice and they should look for a good enough career choice. The subscale of Agentic Creation measures the extent to which an individual believes that the goodness of a career choice is open to change and depends on choice implementation. Participants rated the items of the CBCD on a 7-point Likert-type scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Higher scores indicated stronger CBCD. The current sample revealed α coefficients of .79 and .80 for the two subscales of the CBCD (i.e., Satisficing Decision and Agentic Creation), respectively.

Career Decision Ambiguity Tolerance Scale–Revised (CDAT-R)

The 20-item CDAT-R (Xu & Tracey, 2015, 2017a) was developed to measure career decision ambiguity management, which was defined as people’s evaluations of and responses to unfamiliar, complex, inconsistent, and unpredictable information in career decision-making. The scale contains four subscales: Preference (5 items), Tolerance (5 items), Confidence (5 items), and Aversion (5 items). Preference measures an individual’s tendency to feel interested in and excited about ambiguity in career decision-making (e.g., “I am interested in exploring the many aspects of my personality and interests”). Tolerance measures an individual’s tendency to experience acceptance of ambiguity in career decision-making (e.g., “I am tolerant of the unpredictability of a career”). Confidence measures an individual’s tendency to feel competent in coping with ambiguity in career decision-making (e.g., “I am confident in tackling complex career decision-making tasks”). Aversion measures an individual’s tendency to avoid and withdraw from ambiguity in career decision-making (e.g., “I try to avoid complicated career decision-making tasks”). Participants were invited to rate each item on a 7-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). Higher scores indicated greater endorsement of each factor. Xu and Tracey (2017a) reported α coefficients of .90, .84, .86, and .82 for the four subscales of Preference, Tolerance, Confidence, and Aversion, respectively. The validity of the CDAT-R has been supported in its cross-sectional and longitudinal association with career indecision, career decision-making self-efficacy, and career adaptability (Xu & Tracey, 2015, 2017c). The current study found α coefficients of .89, .81, .82, and .80 for the four subscales of Preference, Tolerance, Confidence, and Aversion, respectively.

Career indecision profile–Short (CIP-Short)

The 20-item CIP-Short (Xu & Tracey, 2017b) was developed to measure an individual’s career indecision resulting from four sources: neuroticism/negative affectivity (NNA), choice/commitment anxiety (CC), lack of readiness (LR), and interpersonal conflicts (IC). Among the four domains, NNA measures general anxiety and neuroticism (e.g., “Often feel fearful and anxious”), CC measures difficulty and anxiety in committing to a single career choice (e.g., “Can’t commit, don’t know other options”), LR measures difficulty in initiating a career decision-making process (e.g., “I am quite confident that I will be able to find a career in which I’ll perform well”), and IC measure difficulty resulting from disagreement with important people in life (e.g., “Important people disagree with plans”). Participants rated the items of the CIP-Short on a 6-point Likert-type scale ranging from 1 (completely disagree) to 6 (strongly agree), with higher scores indicating higher levels of indecision. Xu and Tracey (2017b) reported α coefficients of .84, .84, .82, and .89 for the four factors of career indecision, respectively. The CIP-Short was also found to be highly associated with the parental CIP-65. Its convergent and divergent validity was supported by its theoretically prescribed and empirically demonstrated associations with other indecision measures (Xu & Tracey, 2017b). The current study revealed α coefficients of .86, .82, .82, and .87 for the four factors of career indecision.

Career Adapt-Abilities Scale–USA Form (CAAS)

The 24-item CAAS (Savickas & Porfeli, 2012) was developed to measure an individual’s psychological resources for coping with developmental vocational tasks, occupational transitions, or work traumas. The scale consists of four factors: concern, control, curiosity, and confidence. Savickas and Porfeli (2012) defined concern as being concerned about one’s vocational future (e.g., “Preparing for the future”), control as taking responsibility for one’s vocational future (e.g., “Making decisions by myself”), curiosity as displaying curiosity in collecting information (e.g., “Becoming curious about new opportunities”), and confidence as strengthening the confidence to pursue pursuing one’s aspiration (e.g., “Working up to my ability”). Participants were asked to rate the items of the CAAS on a 5-point Likert-type scale ranging from 1 (not strong) to 5 (strongest), with higher total scores indicating greater career adaptability. Research has demonstrated consistent support for the reliability of the CAAS (e.g., Porfeli & Savickas, 2012; Savickas & Porfeli, 2012). Rudolph et al. (2017) recent meta-analysis also supported the convergent validity of the CAAS in its prediction of adaptive behaviors and adaptation results. The current study found a α coefficient of .94 for the CAAS total scale.

Procedure

Study 2 followed the same procedure as described in Study 1, and participants were also recruited through Amazon MTurk. To enhance validity of participant responses, we only invited participants who had a history approval rate of greater than 90% on previous MTurk assignments. Of the 310 individuals who submitted their answers, 292 passed the validity screening test (e.g., please choose “3”), and those responses were retained for the final analysis. The final data set did not detect missing values for analysis variables.

Analysis

The final version of the CBCD from Study 1 was subjected to CFA, in which we comparatively examined four competing models to more precisely identify the optimal data representation: a one-factor model, a two-factor oblique model, a two-factor orthogonal model, and a three-factor bi-factor model (two specific factors and one general factor). These models have different implications regarding how to interpret the factorial structure and factor scores of the CBCD and are therefore worth empirical investigation (e.g., the bi-factor model supports the direct derivation of a total score based on items, while the two-factor orthogonal model does not directly support this). Based on Hu and Bentler’s (1999) recommendation, the fit of the models was evaluated using the criteria of robust χ², CFI (>.90), RMSEA (<.80), and SRMR (<.80). To render the statistical tests robust to nonnormality, the robust maximum likelihood parameter estimation was adopted.

To examine the incremental validity of the CBCD, we conducted hierarchical multiple regression (HMR) with the four factors of the CIP-Short and the four factors of the CDAT-R as criterion variables. In each HMR analysis, the CAAS scores functioned as the first-level predictor, and the two factors of the CBCD entered the regression as the second-level predictors. We specifically examined whether the CBCD scores could significantly increase the variance accounted for.

Results

Structural Validity

Table 2 summarizes the CFA results. As indicated by the values of CFA (0.48), RMSEA (.162), and SRMR (.17), the one-factor model did not demonstrate an adequate representation of the data. As shown by the values of CFA (0.91 and .91), RMSEA (.068 and .069), and SRMR (.07 and .07), both the two-factor orthogonal and the two-factor oblique models were found to fit the data adequately. However, compared to the two-factor oblique model, the two-factor orthogonal model showed almost identical goodness of fit with fewer model parameters. Meanwhile, the bi-factor model failed to reach convergence in model estimation, indicating model misspecification. Therefore, we endorsed the two-factor orthogonal model as the final model representing the CBCD structure, which cross-validated the EFA results and further supported the structural validity of the CBCD.

Table 2.

Summary of Model Fit Indices for EFA and CFA Models.

Model	χ²	df	BIC	CFI	RMSEA		SRMR
Model	χ²	df	BIC	CFI	Estimate	90% CI.	SRMR
CFA (n = 292)
One-factor model	469.24	54	12,537.73	0.49	0.162	[.149, .176]	0.17
Two-factor orthogonal model	127.25	54	12,120.81	0.91	0.068	[.053, .084]	0.07
Two-factor oblique model	127.11	53	12,126.45	0.91	0.069	[.054, .085]	0.07

Note. CFA = confirmatory factor analysis; BIC = Bayesian information criterion; CFI = comparative fit index; RMSEA = root mean square error of approximation; EFA = exploratory factor analysis. In the CFA, the three-factor bi-factor model was not listed because it failed to achieve convergence.

Incremental Validity

Table 3 reports the means, SDs, and correlations of analysis variables. We calculated the mean scores of the subscales of the CIP-Short and the CDAT-R to represent each factor of career indecision and career decision ambiguity management. Table 4 presents the results of HMR on career indecision and career decision ambiguity management. As seen by the significant ΔF test results across criteria, the CBCD additively predicted NNA, ΔF(1,289) = 8.23, p < .05; CC, ΔF(1,289) = 20.38, p < .05; LR, ΔF(2,288) = 23.42, p < .05; IC, ΔF(2,288) = 26.64, p < .05; preference, ΔF(2,288) = 47.04, p < .05; tolerance, ΔF(2,288) = 24.66, p < .05; confidence, ΔF(2,288) = 19.73, p < .05; and aversion, ΔF(2,288) = 37.34, p < .05, over and beyond career adaptability. Therefore, the results together supported the incremental validity of the CBCD well.

Table 3.

Means, SDs, and Correlations of Analysis Variables.

	Mean	SD	CBCD-S	CBCD-A	NNA	CC	LR	IC	CDAT-P	CDAT-T	CDAT-C	CDAT-A
CBCD-S	3.66	1.22	1.00
CBCD-A	5.29	0.93	−0.03	1.00
NNA	3.54	1.33	0.23**	−.04	1.00
CC	3.58	1.20	0.30**	.07	.67**	1.00
LR	2.27	0.89	0.19**	−.50**	.33**	.28**	1.00
IC	3.01	1.36	0.42**	−.04	.46**	.55**	.27**	1.00
CDAT-P	5.98	0.99	−0.24**	.57**	−.10	−.05	−.56**	−.16**	1.00
CDAT-T	5.44	1.12	0.00	.46**	−.01	.17**	−.34**	04	.58**	1.00
CDAT-C	5.07	1.04	0.12*	.41**	−.29**	−.12*	−.52**	−.02	.50**	.54**	1.00
CDAT-A	4.24	1.21	0.45**	.05	.51**	.48**	.21**	.41**	−.05	.04	−.09	1.00
CAAS	3.89	0.66	−0.18**	.43**	−.43**	−.34**	−.56^**	−.26**	.48**	.31**	.46**	−.25**

Note. N = 292. CBCD-S and A = constructivist beliefs in career decision-making-satisficing decision and agentic creation; NNA = neuroticism/negative Affectivity; CC = choice/commitment anxiety; LR = lack of readiness; IC = interpersonal conflicts; CDAT-P to A = Career Decision Ambiguity Tolerance Scale–Preference, Tolerance, Confidence, and Aversion, respectively; CAAS = Career Adapt-Abilities Scale.

*p < .5. **p < .1.

Table 4.

Results of the Hierarchical Multiple Regression.

Step	Variable	B	SE	β	R ²	ΔF	Step	Variable	B	SE	β	R ²	ΔF
NNA								CDAT-P
1	CAAS	−.87	.11	−.43*	.19	67.07*	1	CAAS	.72	.08	.48*	.23	86.97*
2	CAAS	−.82	.11	−.41*	.21	8.23*	2	CAAS	.38	.08	.25*	.42	47.04*
	CBCD-S	.17	.06	.15*				CBCD-S	−.14	.04	−.18*
	CBCD-A	—	—	—				CBCD-A	.48	.05	.45*
CC								CDAT-T
1	CAAS	−.61	.10	−.34*	.12	37.55*	1	CAAS	.52	.09	.31*	.10	30.42*
2	CAAS	−.53	.10	−.29*	.17	20.38*	2	CAAS	.24	.10	.14*	.23	24.66*
	CBCD-S	.24	.05	.25*				CBCD-S	.03	.05	.04
	CBCD-A	—	—	—				CBCD-A	.48	.07	.40*
LR								CDAT-C
1	CAAS	−.75	.07	−.56*	.31	129.36*	1	CAAS	.73	.08	.46*	.21	78.83*
2	CAAS	−.53	.07	−.39*	.41	23.42*	2	CAAS	.62	.09	.39*	.31	19.73*
	CBCD-S	.08	.03	.10				CBCD-S	.17	.04	.20*
	CBCD-A	−.32	.05	−.33*				CBCD-A	.28	.06	.25*
IC								CDAT-A
1	CAAS	−.53	.12	−.26*	.07	20.58*	1	CAAS	−.46	.10	−.25*	.06	19.37*
2	CAAS	−.44	.12	−.22*	.21	26.64*	2	CAAS	−.32	.10	−.17*	.23	64.41*
	CBCD-S	.43	.06	.38*				CBCD-S	.42	.05	.42*
	CBCD-A	.09	.09	.06				CBCD-A	—	—	—

Note. N = 292. CBCD-S and A = Constructivist beliefs in career decision-making-satisficing decision and agentic creation; NNA = neuroticism/negative affectivity; CC = choice/commitment anxiety; LR = lack of readiness; IC = interpersonal conflicts; CDAT-P to A = Career Decision Ambiguity Tolerance Scale–Preference, Tolerance, Confidence, and Aversion, respectively; CAAS = Career Adapt-Abilities Scale. For NNA, CC, and CDAT-A, CBCD-A did not enter the regression to avoid a suppression effect, which artificially elevated the significance of CBCD-A.

*p < .5.

Discussion

The present study constructed a scale measuring an individual’s CBCD, which are defined as beliefs regarding the goal of career decision-making and the reality of a good career choice. In Study 1, the results revealed a two-factor structure of the newly developed CBCD: satisficing decision and agentic creation. In Study 2, the results supported the internal consistency reliability of the CBCD and cross-validated the two-factor structure of the CBCD. Additionally, the results found the additive predictions of the CBCD for career indecision and career decision ambiguity management over and beyond career adaptability.

Structural and Incremental Validity

The current study revealed a two-factor structure of the CBCD in line with the conceptual two factors of CBCD. Both EFA and CFA indicated that the current items of the CBCD could validly measure the proposed two factors of satisficing decision and agentic creations, which supported the structural validity of the CBCD. The results additionally demonstrated that satisficing decision and agentic creations were independent of one another, suggesting that they tapped into two distinct facets of CBCD. Borrowing the notion of anchor and adjustment from decision-making science (Hastie & Dawes, 2010), we propose that satisficing decision might address the anchor of career decision-making in a goodness hierarchy (i.e., a good enough choice or the best choice), whereas agentic creation might address the extent of allowed adjustment on a career choice (i.e., a fixed impact or a fluid impact of a choice). As the current study adopted a dimensional perspective in investigating constructivist beliefs, it would be interesting for future research to explore the combined effects of these two factors on career decision-making. In other words, a typology/categorical system based on these two orthogonal dimensions might be able to provide meaningful predictions for people’s career decision-making process and outcomes.

In addition to its structural validity, the CBCD’s additive prediction for career indecision and career decision ambiguity management beyond career adaptability also added confidence to its theoretical and practical meaning. The results indicated that the CBCD improved the R ² by 2% for NNA, 5% for CC, 10% for LR, 14% for IC, 19% for preference, 13% for tolerance, 10% for confidence, and 20% for aversion. This consistent and pervasive pattern of additive predictions for important decision-making process and outcome variables provided strong evidence for the incremental validity of the CBCD beyond career adaptability.

Individual Predictions

Although the two factors of the CBCD were found to collectively contribute to the explanation of career indecision and career decision ambiguity management, the nuanced role of the individual factors of satisficing decision remains open for future research. Schwartz et al. (2002) argued that a satisficing orientation might help reduce cognitive burden in decision-making, leading to satisfaction and happiness. Hacker, Carr, Abrams, and Brown (2013) have also proposed that a satisficing orientation might help reduce difficulties in career decision-making. However, the current results did not find anticipated support for the conjecture of satisficing decision-making being a recommended cognitive strategy for career decision-making, as this belief was found to be positively associated with career indecision and ambiguity aversion.

This phenomenon might be attributable to cultural norms regarding career decision-making. We speculated that satisficing decision-making could be culturally regarded as a passive and dysfunctional approach for Western career decision-makers, leading them to avoid this strategy during career decision-making but use it to justify their career indecision. In other words, satisficing decision-making could be a product of career indecision in Western cultural contexts. However, in more dialectical cultural contexts (e.g., East Asia) where pursuing a good enough option is often regarded as a practice of wisdom (Bai, 2005), satisficing decision-making might be an actively chosen strategy to navigate ambiguous career decision-making. Certainly, future research from a cross-cultural perspective will help examine this conjecture.

Although satisficing decision exhibited significant but directionally unexpected predictions for criterion variables, agentic creation demonstrated theoretically prescribed predictions for criterion variables. We particularly noticed that agentic creation had strong predictions for LR (r = −.50), preference (r = .57), tolerance (r = .46), confidence (r = .41), and career adaptability (r = .43). The results thus suggested that individuals who believe that they should actively construct a good career path tend to have more motivation for career decision-making and find ambiguity more interesting, acceptable, and manageable. Therefore, counselors could focus their treatment on this aspect of constructivist beliefs to increase their clients’ motivation and adaptive reactions to ambiguity in career decision-making.

Implications for Career Construction Theory and Career Counseling

The development of the CBCD has important implications for both career construction theory and career counseling. Because research about career construction has predominantly focused on construction resources-career adaptability (e.g., Ginevra et al., 2016; Hirschi et al., 2015; Rudolph et al., 2017), the current study demonstrated that constructivist beliefs could also play a salient role in career decision-making. With this evidence, we further argue that an examination of the role of career construction in career decision-making is incomplete without taking constructivist beliefs into account because constructivist beliefs closely (if not more than construction resources) align with the central tenet of career constructivism: whether a good career choice is constructed. This key foundation of career construction theory differs from and complements previous career theories, such as P-E correspondence models, which emphasize the discovery of a good (if not the best) choice (Savickas, 2015; Savickas et al., 2009). Therefore, we suggested that future research continue to use the CBCD to investigate how constructivist beliefs influence the process and outcome of career decision-making and career development. To further advance research about career construction theory, it would also be necessary for future research to examine the antecedents of constructivist beliefs. Several personal and environmental factors could be plausible candidates such as personality (Nussbaum & Bendixen, 2003), cultural norms (Chan & Elliott, 2004), and even interventions (Kienhues, Bromme, & Stahl, 2008).

In addition to its implication for career construction theory, the current study suggested that the CBCD could be a reliable and valid measure of a client’s constructivist beliefs in career counseling. Although there has been an increasing voice in the field advocating a focus on a client’s subjective construction in assessment interpretation and career planning (Busacca & Rehfuss, 2016; Savickas, 1995; Savickas et al., 2009), much less attention has focused on a client’s beliefs regarding career construction. However, a client’s constructivist beliefs could play a salient role in the process and outcome of career decision-making, as shown by this study. The lack of attention to constructivist beliefs is an important area to address, particularly for career counseling, because formal assessment of a client’s constructivist beliefs could have provided career counselors with clues and guidance regarding treatment prognosis (i.e., how construction-oriented career counseling would work) and treatment focus (i.e., whether constructivist beliefs should be a clinical focus). Therefore, the current study provided a psychometrically sound tool to help career counselors assess a client’s important constructivist beliefs, which have not been sufficiently addressed in existing measures of career decision-making (e.g., the CBI and CTI; Kleiman et al., 2004; Walsh et al., 1997). The current results suggested that counselors can assess clients’ satisficing decision to indicate their levels of career indecision and ambiguity aversion. Additionally, counselors can assess clients’ agentic creation to understand their struggle with motivation and adaptive reactions to ambiguity in career decision-making. In career assessment contexts, we recommend that counselors use the CBCD scores to determine whether a constructivist approach to career counseling fits a client’s expectations of career decision-making/counseling and choose appropriate interventions accordingly.

Limitations and Suggestions

There are several limitations to be noted when interpreting the current results. First, the studies focused on a general population who were relatively young. Although the current samples have more representativeness of career decision-makers than traditional college samples, the results might not be generalizable to individuals at a later stage of career development. Therefore, a further validation of the CBCD in mid-career workers appears necessary. Second, the current studies used cross-sectional data. Therefore, the causal relations of people’s constructivist beliefs to their career decision ambiguity management and career indecision have not been thoroughly established, and an experimental design would be particularly helpful for understanding the causality between satisficing decision and career indecision. Finally, while the current study focused on an important question of whether constructivists beliefs could facilitate the process of career decision-making, it would be interesting to see future research exploring how constructivist beliefs predict people’s career choices.

Conclusion

Based on career construction theory (Savickas, 2013; Savickas et al., 2009), the current study developed and initially validated an instrument measuring a client’s constructivist beliefs regarding the goal of career decision-making and the reality of a good career choice. In general, the results supported the reliability, structural validity, and incremental validity of the newly developed CBCD. Given its important associations with career indecision and career decision ambiguity management, researchers can use this new tool to supplement career adaptability in examining the role of a constructivist perspective in career decision-making. Additionally, clinicians can use this tool to diagnose a client’s difficulty in career decision-making and make appropriate treatment plans. Overall, the current study proposes the unique importance of CBCD and contributes to the research and practice of career construction theory by providing a useful tool for assessing constructivist beliefs.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Hui Xu

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