Russian Version of the Career Crafting Assessment (CCA): Psychometric Testing and Validation

Yakukhnova, A.S. Saint Petersburg State University, Russia

Mararitsa, L.V. Saint Petersburg State University, Russia

Gurieva, S.D. Saint Petersburg State University, Russia

Introduction

Contemporary Career Context

Contemporary labor markets are characterized by increasing instability and non-linear career trajectories, which place greater responsibility for career development on individuals. Structural transformations of work – including technological change and the growing impact of artificial intelligence – are reshaping employment conditions and increasing uncertainty, influencing both work experiences and perceptions of job security (Xu et al., 2026). As a result, maintaining employability, professional growth, and psychological sustainability increasingly depends on individuals’ ability to actively regulate their career trajectories through proactive and adaptive behavior (De Vos et al., 2019; Forrier et al., 2018; Greenhaus et al., 2024).

Recent career research frames these challenges through the lens of sustainable careers, understood as career development shaped by the interaction of person, context, and time, and reflected in health, well-being, and productivity (De Vos et al., 2020; Greenhaus et al., 2024). More broadly, sustainable development has been proposed as a fourth paradigm for career science and practice, emphasizing decent work, well-being, and social responsibility (Hartung & Di Fabio, 2024). Within this framework, career crafting can be viewed as a proactive form of career regulation through which individuals intentionally shape career-related resources, relationships, meanings, and task boundaries.

Career Development Theories as Precursors to Career Crafting

Research on career development has consistently emphasized the active role of individuals in shaping their career trajectories through goal-directed behavior and self-regulation. Contemporary approaches conceptualize careers as dynamic and self-directed processes in which individuals engage in actions such as exploring opportunities, developing competencies, and managing transitions across changing work contexts (Akkermans et al., 2013; Forrier et al., 2018; Jackson & Tomlinson, 2020). These perspectives emphasize that career development depends not only on structural conditions, but also on individuals’ capacity to proactively influence their career paths through observable behavior.

A complementary line of research focuses on adaptive and meaning-making processes underlying career development. These approaches emphasize the role of psychological resources, identity construction, and alignment between career trajectories and personal values in sustaining long-term development (Akkermans & Tims, 2017; De Vos et al., 2019). These two lines of research – behavioral and adaptive – jointly frame careers as actively shaped through both proactive actions and ongoing self-regulation, providing the basis for later conceptualizations of career crafting.

Career Crafting as an Extension of the Crafting Perspective

Career crafting emerged at the intersection of career development research and job crafting as an attempt to conceptualize proactive, behaviorally enacted regulation at the level of long-term career development, thereby integrating career agency frameworks with the crafting paradigm.

The concept of job crafting was introduced by Wrzesniewski and Dutton (2001) and refers to proactive changes employees make in their tasks, relationships, and perceptions of work in order to improve person–job fit and work experience. Two complementary approaches to job crafting can be distinguished. The first, role-based perspective, conceptualizes job crafting as changes in task, relational, and cognitive boundaries of work (Wrzesniewski & Dutton, 2001). The second, grounded in the Job Demands–Resources framework, defines job crafting as self-initiated modifications of job demands and resources aimed at improving functioning and motivation (Bakker & Demerouti, 2014; Tims et al., 2012).

The term “career crafting” first appeared in the literature as a career-level extension of the crafting concept (Vidwans, 2016). Its conceptual development was subsequently advanced by Akkermans and Tims, who linked career crafting to research on career competencies and proactive career behavior and framed it as a mechanism of active career self-regulation through which individuals influence their career outcomes (Akkermans & Tims, 2017). Building on this work, De Vos and colleagues positioned career crafting within contemporary career studies as a process through which individuals actively shape their career paths by integrating developmental, relational, and contextual influences across time (De Vos et al., 2019). These contributions established career crafting as a distinct construct describing proactive career-related behavior enacted over time.

Career crafting can be defined as proactive behaviors through which individuals intentionally influence the direction, meaning, and development of their careers in alignment with personal goals and contextual opportunities (Akkermans & Tims, 2017; De Vos et al., 2019). In contrast to job crafting, which concerns modifications within a current organizational role, career crafting operates at the level of broader career trajectories and includes actions related to transitions, developmental pathways, opportunity creation, and professional identity construction (De Vos et al., 2019).

Several theoretical streams contribute to the conceptualization of career crafting. One important foundation derives from research on career competencies and proactive career behavior, emphasizing individuals’ active roles in managing career development through goal-directed actions and adaptive strategies (Akkermans et al., 2013; Akkermans & Tims, 2017). Another pathway originates from the extension of job crafting principles toward career-level processes, integrating environmental, relational, and personal factors influencing career pathways (De Vos et al., 2019; Vidwans, 2016). In addition, resource-based perspectives, including Conservation of Resources theory, provide a motivational explanation for proactive career regulation by conceptualizing such behavior as efforts to acquire, maintain, and invest resources necessary for achieving valued career goals (Hobfoll, 1989; Hobfoll et al., 2018). These perspectives suggest that career crafting represents a multidimensional regulatory process shaped by personal agency, resource dynamics, and contextual opportunities across time.

Career crafting is conceptually related to job crafting, career competencies, career self-management, and proactive personality, yet it represents a distinct domain within career research. It differs from job crafting in its level of analysis, from career competencies in its focus on enacted behavior (Akkermans et al., 2013; Akkermans & Tims, 2017), and from career self-management and proactive personality in its domain specificity and process orientation (Crant, 2000; De Vos et al., 2019; King, 2004). These distinctions support the conceptualization of career crafting as a distinct behavioral construct within career research.

Measurement Instruments for Career Crafting

Empirical measurement of career crafting is relatively recent and less developed than the measurement of related constructs such as job crafting, and the number of dedicated instruments is extremely limited (Balakhtar et al., 2024). The literature reflects two conceptual approaches to operationalizing career crafting.

The earlier approach is represented by the competence-based framework proposed by Tims and Akkermans, embedded within broader career self-management traditions and operationalized through proactive career reflection and proactive career construction aimed at improving person–career fit (Akkermans & Tims, 2017; Tims & Akkermans, 2020). This perspective conceptualizes career crafting primarily through career competencies and regulatory processes associated with adaptive career development (Akkermans et al., 2013).

At the measurement level, however, the availability of dedicated instruments remains limited. To date, the Career Crafting Assessment (CCA) remains the only dedicated and psychometrically validated instrument specifically developed to assess career crafting as a distinct behavioral construct. This instrument is grounded in a behavioral conceptualization of career crafting and differs from the competence-based approach by directly measuring enacted career-related behaviors.

The conceptual foundation of the instrument is consistent with the sustainability-oriented view of careers, because it integrates two complementary mechanisms associated with sustainable career development: proactivity, referring to intentional actions aimed at expanding career-relevant resources and opportunities across time and contexts; and congruence, referring to efforts to maintain alignment between career development and individuals’ interests, values, and psychological needs (Lee et al., 2021). Within this framework, career crafting is operationalized as a set of proactive and congruence-seeking behaviors through which individuals actively shape their career trajectories while striving for meaningful person–career fit. Importantly, the CCA does not measure career sustainability directly; rather, it assesses behavioral processes through which individuals may construct career trajectories that are more meaningful, adaptive, and aligned with personal and contextual resources.

The CCA has demonstrated satisfactory psychometric properties and increasing empirical use, and cross-cultural validation studies are gradually emerging, including Romanian and Turkish adaptations (Balakhtar et al., 2024; Chifor & Oprea, 2023; Kilic & Kitapci, 2025; Lee et al., 2021). Nevertheless, the overall measurement landscape remains limited, and further validation across occupational groups and cultural contexts is needed (Balakhtar et al., 2024).

A more detailed description of the conceptual structure and psychometric properties of the Career Crafting Assessment is presented in the following section.

The Career Crafting Assessment (CCA): Conceptual Foundations and Empirical Validation

The Career Crafting Assessment (CCA) was developed to measure career crafting as a distinct behavioral construct. Item generation drew on multiple conceptual domains relevant to career behavior, including job crafting, career planning, career self-management, career salience, and value-driven career orientations, with items reformulated to capture observable behaviors at the career level rather than dispositional tendencies (Lee et al., 2021).

Initial item development involved 96 candidate items derived from existing career-related scales, which were reduced through expert review and empirical testing to 50 items and subsequently refined through exploratory factor analysis to a final set of 15 items (Lee et al., 2021). Responses are rated on a 7-point Likert scale ranging from “does not describe me at all” to “completely describes me.”

The final version of the CCA represents a multidimensional construct composed of four interrelated dimensions organized within a higher-order career crafting factor (Lee et al., 2021):

1. Changing Relational Boundaries. Captures behaviors directed at expanding one’s professional network and obtaining new external perspectives for career development, such as initiating new contacts and participating in career-related events.
2. Utilizing Relational Resources. Reflects proactive behaviors aimed at establishing and developing relationships with individuals who possess valuable career-related knowledge or influence, including seeking mentorship, professional advice, and developmental feedback.
3. Reflecting Positive Career Meaning. Represents the cognitive-motivational component of career crafting and reflects active interpretation of one’s career as meaningful and personally significant, including viewing career experiences as sources of self-expression and life satisfaction.
4. Expanding Task Boundaries. Assesses proactive modification and expansion of professional roles and responsibilities, including voluntarily taking on new tasks or responsibilities aligned with long-term career goals.

Exploratory factor analysis identified four factors explaining 67% of the total variance, with high internal consistency for the overall scale (Cronbach’s α = .93; McDonald’s ω = .95) and subscale reliabilities ranging from α = .83 to .93 and ω = .84 to .93. Confirmatory factor analysis supported both the correlated four-factor model and the hierarchical four-factor model. The higher-order model demonstrated good fit (CFI = .97, TLI = .961, SRMR = .05, RMSEA = .07, 90% CI [.05, .08]), with strong loadings of the four subdimensions on the higher-order career crafting factor (.64–.89) (Lee et al., 2021).

Evidence for construct validity was supported through positive associations with theoretically related constructs, including career exploration, career self-management, organizational career management, protean career orientation, and job crafting. Career crafting also demonstrated incremental validity beyond job crafting in predicting meaningful work and work engagement, indicating that career-level proactive regulation captures unique variance in adaptive functioning (Lee et al., 2021).

Subsequent research has further supported the relevance of career crafting for psychological functioning and career outcomes, including associations with employee well-being and identity-related mechanisms (Li et al., 2025), as well as evidence that career crafting behaviors can be enhanced through targeted interventions (Van Leeuwen et al., 2021).

Despite growing empirical interest, validated versions of the CCA are currently available only in a limited number of languages, including Romanian and Turkish, highlighting the need for further cross-cultural adaptation and validation.

Aim and Objectives of the Study

The aim of the present study was to adapt and validate a Russian version of the Career Crafting Assessment (CCA), hereafter referred to as the Career Crafting Assessment – Russian version (CCA-R), and to examine its psychometric properties in a Russian-speaking employee sample. The specific objectives were: (1) to examine the factorial structure of the CCA-R and assess its reliability at the scale and subscale levels; (2) to evaluate item-level properties using modern test theory approaches, including Item Response Theory; and (3) to establish convergent validity of the CCA-R by examining its associations with validated measures of job crafting and proactive attitudes.

Methods

Participants

A total of 1.100 respondents from different regions of Russia participated in the study. The largest proportions of participants were from Moscow and the Moscow region (19.5%) and from St. Petersburg and the Leningrad region (7.5%). The sample was predominantly female (65.5%). Participants’ age ranged from 18 to 73 years (M = 39.9, SD = 10.6). Most respondents had completed higher education (60.5%), while 20.9% had secondary vocational education and 3.9% reported holding an academic degree or an MBA.

In terms of occupational characteristics, the sample primarily consisted of employees (47.4%), with the remaining participants occupying managerial positions. The majority of respondents were salaried employees (81.2%), while 18.8% were self-employed, including freelancers and entrepreneurs. Most participants worked full-time (78.7%). Regarding working time, 52.4% reported working up to 50 hours per week, while 25.2% reported working more than 50 hours per week.

Measures

Primary Measure: Career Crafting Assessment

The Career Crafting Assessment (CCA; Lee et al., 2021) was used to assess career crafting. The instrument consists of 15 items forming four subscales. The full English and Russian versions of the questionnaire, along with the scoring key, are provided in Appendix A (Tables A1, A2).

Respondents indicated the extent to which each statement described them using a 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). In the original version of the instrument, responses were recorded on a 7-point Likert scale ranging from 1 (Does not describe me at all) to 7 (Completely describes me). Subscale scores were calculated as the arithmetic mean of the corresponding items.

The Russian translation of the Career Crafting Assessment was conducted by four experts with advanced degrees in social psychology who were familiar with the construct. Items were translated independently and subsequently discussed in order to reach consensus in cases of discrepancy. The translation procedure was guided by the principle of semantic equivalence, rather than literal correspondence, to ensure conceptual consistency with the original instrument.

Measures Used to Examine Convergent Validity

To examine the convergent validity of career crafting as a proactive behavioral construct, several additional instruments were administered.

The Proactive Attitude Scale (PAS) (Russian adaptation PAS-R by Bekhter & Filatova, 2022) was used to assess a stable proactive orientation toward life in general. The scale consists of 8 items rated on a 4-point Likert scale ranging from 1 (Strongly disagree) to 4 (Strongly agree). The Russian version of the PAS has demonstrated satisfactory reliability and validity.

The Job Crafting Scale (JCS) (Tims, et al., 2012; Russian adaptation JCS-R by Manichev et al., 2023) was employed to assess proactive changes in work content and working conditions. The scale comprises four subscales: Increasing Structural Job Resources, Increasing Social Job Resources, Increasing Challenging Job Demands, and Decreasing Hindering Job Demands. Each subscale includes between 4 and 6 items, rated on a 5-point scale from 1 (Never) to 5 (Very often). Subscale scores were computed as the arithmetic mean of the respective items.

The Job Crafting Questionnaire (JCQ) (Slemp & Vella-Brodrick, 2013; Russian adaptations JCQ-R by Yumkina et al., 2024) was used to assess alternative operationalizations of job crafting. JCQ consists of 15 items forming three subscales (Task Crafting, Cognitive Crafting, and Relational Crafting), each containing five items. Respondents indicated how frequently they engaged in each behavior using a 6-point Likert scale ranging from 1 (Very rarely) to 6 (Very often).

Procedure

Participants were recruited via the online survey platform anketolog.ru. Eligibility criteria included being employed at the time of participation and being at least 18 years of age. Data collection continued until the planned sample size was reached. Responses demonstrating insufficient quality (e.g., incomplete questionnaires or clearly invalid response patterns) were excluded prior to analysis.

Data were collected in three waves: two waves in 2023 (n = 300 and n = 500) and one wave in 2024 (n = 300), resulting in a total sample of 1,100 respondents. Sample sizes differed across instruments: the CCA and JCQ were administered to all respondents (N = 1,100), the JCS to respondents from the two 2023 waves (n = 800), and the PAS to two subsamples of 300 respondents each (n = 600).

Data Analysis

The analytic strategy combined Classical Test Theory (CTT)-based analyses and Item Response Theory (IRT).

CTT-based analyses included descriptive statistics and data screening, confirmatory factor analysis (CFA) of the expected four-factor and higher-order models, reliability estimation (Cronbach’s alpha, McDonald’s omega, and Schmid–Leiman analysis), and correlation analysis for convergent validity. As an additional check, exploratory factor analysis (EFA) was conducted to examine the reproducibility of the original factor structure in the adapted Russian version, with attention to factor loadings, cross-loadings, and the proportion of explained variance.

IRT was used as a complementary item-level approach to evaluate item discrimination, response thresholds, and latent trait coverage.

Statistical analyses were conducted using R (version 4.4.3) in RStudio (version 2024.12.1+563). CFA was performed in lavaan (version 0.6-19) using Maximum Likelihood (ML) estimation; CFA visualization was carried out with semPlot (version 1.1.6). IRT analysis was performed in mirt (version 1.45.1), with the Graded Response Model (GRM) estimated via the Quasi-Monte Carlo Expectation-Maximization method (QMCEM).

Results

Descriptive Statistics and Data Screening

The means and standard deviations of the Career Crafting Assessment – Russian version (CCA-R) were comparable to those reported for the original instrument by Lee et al. (2021). Skewness values ranged from -.78 to -.32, and kurtosis values ranged from -.43 to 1.01 across the subscales, remaining close to the ±1 threshold. These indices indicate an acceptable approximation to normality and support the use of parametric statistical methods (Nasledov, 2013). Descriptive statistics for all CCA-R subscales are presented in the Appendix B (Table B1).

Prior to factor analysis, sampling adequacy was examined using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s test of sphericity. The overall KMO value was .93, and individual measures of sampling adequacy (MSA) ranged from .88 to .96, indicating good item adequacy. Bartlett’s test of sphericity was statistically significant, χ²(105) = 8,947, p < .001, confirming that the correlation matrix significantly deviated from an identity matrix (Tabachnick & Fidell, 2007). Examination of inter-item correlations revealed no evidence of problematic multicollinearity, with Pearson correlation coefficients ranging from 0.26 to .70. Overall, the data met established methodological criteria for factor analysis.

Factor Analysis

At the first stage, EFA examined alternative three- and four-factor solutions. The four-factor solution accounted for 61% of the variance and was retained for further confirmatory testing. The EFA results are reported in Appendix C (Tables C1, C2).

At the next stage, CFA was used to further evaluate the factorial structure of the CCA-R. Three competing models were tested: (1) a four-factor model, (2) a four-factor model with a second-order latent career-crafting factor, and (3) a three-factor model. The CFA results are summarized in Table 1.

Table 1

Fit Indices for CFA Models

No.	Model	χ²/df	CFI	TLI	RMSEA [90% CI]	SRMR	AIC
1	Four-factor model	8.29	.931	.914	.081 [.076–.087]	.053	39.337
2	Four-factor model with a second-order factor	8.90	.924	.907	.085 [.079–.090]	.064	39.402
3	Three-factor model	9.06	.921	.905	.086 [.080–.091]	.056	39.424

Notes. χ²/df = chi-square divided by degrees of freedom. CFI = Comparative Fit Index. TLI = Tucker–Lewis Index. RMSEA [90% CI] = Root Mean Square Error of Approximation (with 90% confidence interval). SRMR = Standardized Root Mean Square Residual. AIC = Akaike Information Criterion. 

The four-factor model demonstrated the best fit among the tested models (CFI = .931, TLI = .914, RMSEA = .081, SRMR = .053, AIC = 39,338). The higher-order model showed slightly lower but comparable fit indices (CFI = .924, TLI = .907, RMSEA = .085, SRMR = .064, AIC = 39,403). The difference in TLI between the four-factor and higher-order models was small (ΔTLI < .01), suggesting comparable overall fit (Gignac, 2007).

Given the theoretical conceptualization of career crafting as an integrative construct and the consistency of the obtained results with the original validation study (Lee et al., 2021), the higher-order factor model was retained as the theoretically preferable representation of the CCA-R structure. The final factor structure is illustrated in Figure 1.

Figure 1. Four-factor model of the CCA-R with a higher-order factor

Notes. Ovals: latent variables – Changing Relational Boundaries (CHG), Utilizing Relational Resources (RES), Reflecting Positive Career Meaning (POS), Expanding Task Boundaries (TAS), and the second-order latent factor Career Crafting (CCA). Rectangles: observed item – CHG1-CHG4, RES1-RES4, POS1-POS4, TAS1-TAS3. Long single-headed arrows:  factor loadings (from latent variables to items) and second-order loadings (from CCA to first-order factors); values are standardized. Short single-headed arrows: residuals/errors (attached to items; if shown on factors, indicate factor residuals).

Reliability

Cronbach’s α and McDonald’s ω for the total CCA-R score were .92 and .95, respectively, indicating high internal consistency. All subscales also demonstrated satisfactory reliability. Cronbach’s α and total ω coefficients for the subscales were as follows: Changing Relational Boundaries: α = .88, ω = .88; Utilizing Relational Resources: α = .82, ω = .82; Reflecting Positive Career Meaning: α = .81, ω = .82; and Expanding Task Boundaries: α = .78, ω = .79.

Reliability estimates based on the Schmid–Leiman analysis further supported the internal consistency of the scale. The hierarchical omega coefficient (ωh), reflecting the contribution of the general factor, was .77, with an asymptotic value of .82, indicating a substantial role of the overarching latent construct. The explained common variance attributable to the general factor was 61.8%, suggesting the presence of a robust shared component across items. The mean contribution of the general factor to item variance was .62 (SD = 0.13).

Overall, these results indicate high reliability and internal consistency of both the total CCA-R score and its subscales, with a meaningful contribution of a general career-crafting factor.

Item-Level Reliability Considerations

IRT models describe the probabilistic relationship between item characteristics, response categories, and the level of an underlying latent trait. Therefore, item-level properties of the CCA-R were examined using the GRM for ordered categorical responses, with parameters estimated via QMCEM (Chalmers, 2012).

Item evaluation focused on two key parameters: (a) discrimination and (b) threshold parameters (b1–b4). The discrimination parameter reflects an item’s ability to differentiate between respondents with different levels of the latent trait, with higher values indicating greater sensitivity to individual differences. Following Baker’s (2001) classification, discrimination values below .65 are considered low, values between .65 and 1.34 moderate, values between 1.35 and 1.69 high, and values of 1.70 or higher very high.

Threshold parameters (b) indicate points along the latent trait continuum at which respondents are most likely to endorse successive response categories. Within the GRM framework, each item is characterized by multiple thresholds corresponding to transitions between adjacent categories. An approximately even distribution of threshold values within the range of -3 to +3 suggests adequate coverage of the latent trait continuum. Thresholds located outside this range may indicate limited sensitivity at certain trait levels or potential inconsistencies in category functioning (Embretson & Reise, 2000).

The results of the IRT (GRM) item analysis are presented in Table 2.

Table 2

Discrimination Parameters and Threshold Ranges

Scale	a	b1	b2	b3	b4
Changing Relational Boundaries	2.54 – 3.58	-2.10 – -1.82	-1.27 – -.99	-.45 – -.14	1.07 – 1.37
Utilizing Relational Resources	1.82 – 3.05	-2.26 – -1.40	-1.13 – -.40	-.24 – .49	1.38 – 2.22
Reflecting Positive Career Meaning	1.55 – 3.80	-2.69 – -2.14	-1.64 – -1.41	-.77 – -.32	.95 – 1.57
Expanding Task Boundaries	1.93 – 3.93	-2.27 – -1.37	-1.36 – -.43	-.35 – .49	1.60 – 1.98

Note. a – discrimination, b1–b4 – threshold values.

The four-factor model demonstrated adequate item discrimination for the majority of items, and threshold parameters were generally well distributed across the latent trait continuum (Baker, 2001). Overall, the items provided satisfactory coverage of different levels of career crafting.

Validity

To examine convergent validity, Pearson’s correlation coefficients were calculated between the Career Crafting Assessment-Russian version (CCA-R) and three Russian-adapted measures: the Job Crafting Questionnaire – Russian version (JCQ-R), the Job Crafting Scale – Russian version (JCS-R), and the Proactive Attitude Scale – Russian version (PAS-R).

Moderate and statistically significant correlations were observed between career crafting and job crafting, as well as between career crafting and proactive attitudes. Specifically, the CCA-R was positively correlated with the JCQ-R (r = .61) and the JCS-R (r = .59). The only exception was the Decreasing Hindering Job Demands subscale of the JCS-R, which showed no meaningful association with the CCA-R total score (r = .04). A moderate positive correlation was also found between the CCA-R and proactive attitudes (r = .41).

Detailed results of the correlation analysis are presented in Table 3.

Table 3

Convergent validity of the CCA-R

No.	Scale	1	2	3	4	5
1	CCA-R (Total)
2	CCA-R – Changing relational boundaries	.87***
3	CCA-R – Utilizing relational resources	.89***	.76***
4	CCA-R – Reflecting positive career meaning	.81***	.59***	.59***
5	CCA-R – Expanding task boundaries	.75***	.50***	.54***	.57***
6	JCQ-R (Total)	.61***	.49***	.49***	.54***	.52***
	└── Task crafting	.49***	.41***	.38***	.39***	.48***
	└── Cognitive crafting	.54***	.40***	.40***	.58***	.44***
	└── Relational crafting	.56***	.49***	.49***	.43***	.45***
7	JCS-R (Total)	.59***	.48***	.52***	.46***	.50***
	└── Increasing structural job resources	.48***	.43***	.36***	.45***	.37***
	└── Increasing social job resources	.45***	.34***	.46***	.30***	.38***
	└── Increasing challenging job demands	.61***	.49***	.48***	.46***	.63***
	└── Decreasing hindering job demands	.04	.03	.09*	.05	-.03
8	PAS-R	.41***	.36***	.28***	.41***	.33***

Notes. The table reports Pearson’s r correlation coefficients. CCA-R and JCQ-R correlations were calculated using N = 1,100; JCS-R correlations using n = 800; PAS-R correlations using n = 600. *p < .05, **p < .01, ***p < .001.

Discussion

The results of the present study provide evidence for the reliability and validity of the Russian version of the Career Crafting Assessment (CCA-R). Consistent with the original English version, the adapted instrument reproduced a four-factor structure comprising Changing Relational Boundaries, Utilizing Relational Resources, Reflecting Positive Career Meaning, and Expanding Task Boundaries.

Confirmatory factor analysis showed that the four-factor model demonstrated the best fit among the tested models (CFI = .931, TLI = .914, RMSEA = .081), whereas the higher-order model showed slightly lower but comparable fit (CFI = .924, TLI = .907, RMSEA = .085). These results support the multidimensional structure of the CCA-R and are consistent with the theoretical conceptualization of career crafting as an integrative construct. Reliability estimates indicated satisfactory to high internal consistency across the subscales, with Cronbach’s α ranging from .78 to .88 and McDonald’s ω ranging from .79 to .88. In EFA, the four-factor solution explained 61% of the variance, which was slightly lower than but still comparable to that reported for the original version (67%). Overall reliability of the adapted scale was high (α = .92, ω = .95) and closely aligned with the original instrument (α = .93, ω = .95) (Lee et al., 2021).

Results of the Schmid–Leiman analysis further supported the presence of a general career-crafting factor, alongside meaningful specific dimensions. The hierarchical omega coefficient was .77, and the explained common variance attributable to the general factor was 61.8%, indicating a substantial shared component across the items. This finding is consistent with the conceptualization of career crafting as an integrative construct and supports both the use of a total score and the differentiated interpretation of subscale scores.

Item-level analyses based on Item Response Theory (IRT) indicated that all items demonstrated high to very high discrimination and provided adequate coverage across the latent trait continuum.

Convergent validity of the CCA-R was supported by moderate correlations with JCQ-R, JCS-R, and PAS-R. Notably, the Decreasing Hindering Job Demands subscale of the JCS-R showed no practically meaningful associations with career crafting dimensions. This pattern is consistent with prior findings indicating that avoidance-oriented job crafting behaviors are conceptually and empirically distinct from proactive forms of crafting and are often unrelated or negatively related to adaptive outcomes such as work engagement and proactive behavior (Bakker et al., 2012; Lichtenthaler & Fischbach, 2019; Tims et al., 2012).

Conclusion

The findings of the present study provide evidence supporting the validity and reliability of the Russian version of the Career Crafting Assessment (CCA-R).

First, the four-factor structure of the instrument – Changing Relational Boundaries, Utilizing Relational Resources, Reflecting Positive Career Meaning, and Expanding Task Boundaries – was reproduced in a Russian-speaking sample. The higher-order representation also remained theoretically meaningful, although its fit was slightly lower than that of the correlated four-factor model. The subscales demonstrated coherent factor structure and conceptual differentiation.

Second, both the subscale scores and the overall career crafting score exhibited good internal consistency, indicating stable measurement properties of the adapted instrument.

Third, item-level analyses based on Item Response Theory provided further support for the psychometric quality of the CCA-R. The results also supported the multidimensional nature of career crafting and the presence of both a general factor and meaningful specific components, consistent with the theoretical model underlying the original version of the scale.

Finally, convergent validity was supported by statistically significant associations with conceptually related constructs, including job crafting and proactive attitudes.

Overall, the results indicate that the CCA-R is a psychometrically sound instrument for assessing proactive career crafting behavior in Russian-speaking samples and can be used in both research and applied contexts.

In future research, the CCA-R may also be used to examine whether career crafting contributes to sustainable career development by supporting long-term well-being, health, employability, and meaningful career continuity across different occupational contexts.

Limitations

First, data were collected using an online survey distributed via large-scale recruitment platforms. As a result, the sample cannot be considered representative of the general population, which may limit the generalizability of the findings and the external validity of the conclusions. Future research could further strengthen the construct validity of the CCA-R by examining its associations with closely related behavioral constructs, such as career competencies and career self-management. These constructs capture complementary aspects of proactive career regulation and would allow for a more precise validation of career crafting as a behavioral construct. The inclusion of such measures, however, remains constrained by the limited availability of validated Russian-language instruments.

Ethics Statement

The study was reviewed and approved by the Ethics Committee of Saint Petersburg State University (Protocol No. 115-02-102 dated September 23, 2025). The study was conducted in accordance with the ethical standards for research involving human participants. Participation was voluntary, and all respondents provided informed consent prior to completing the survey. The data were collected anonymously and analyzed in aggregated form.

Author Contributions

Anastasia Yakukhnova conducted the theoretical review, performed the statistical analyses, and drafted the Results and Discussion sections. Larisa Mararitsa contributed to the research design and edited the manuscript draft. Svetlana Gurieva provided methodological supervision of the project.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgements

This research was supported by Grant No. 22-18-00452-P, “Psychosocial design of the work environment as a factor of employee well-being and organizational innovative potential,” from the Russian Science Foundation.

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Appendix

Appendix A

Career Crafting Assessment – Russian Version (CCA-R)

Instructions. Please indicate the extent to which you agree or disagree with each of the following statements.

___ Strongly disagree

___ Disagree

___ Neither agree nor disagree

___ Agree

___ Strongly agree

Table A1

CCA Items (Russian and English Versions)

No.	Code	Statement (RU)	Statement (EN)
1	CHG1	Я стремлюсь познакомиться с людьми, карьерой которых я восхищаюсь	I make an effort to get to know people whose career I admire
2	CHG2	Я развиваю контакты с людьми, с которыми я могу сотрудничать в интересах своей карьеры	I make connections with people who share my career interests
3	CHG3	Я завязываю контакты с людьми, обладающими теми навыками, которые я хочу развить у себя	I make connections with people who have the skills I want to develop in my career
4	CHG4	Я налаживаю контакты с людьми в тех областях, где я хотел(а) бы работать	I build contacts with people in areas where I would like to work
5	RES1	Я обращаюсь за советом к тем, чьей карьерой восхищаюсь	I seek professional coaching from those whose careers I admire
6	RES2	Я посещаю мероприятия, которые помогают изучить возможные пути карьерного развития	I attend events that will help me explore different career paths
7	RES3	Я прошу познакомить меня с людьми, которые могут положительно повлиять на мою карьеру	I ask others to introduce me to people who can positively influence my career
8	RES4	Я интересуюсь тем, что думают другие по поводу моего карьерного развития	I ask others for feedback on my career development
9	POS1	Я отслеживаю, каким образом моя работа и карьера позитивно влияет на мою жизнь	I think about the ways in which my career positively impacts my life
10	POS2	Я всегда помню, что моя работа и карьера значима для меня лично	I remind myself that my career has personal significance
11	POS3	Я размышляю о том, какую роль моя работа и карьера играет в моём общем благополучии	I reflect on the role my career has for my overall well–being
12	POS4	Я рассматриваю работу и карьеру как способ самовыражения	I look at a career as a means of expressing myself
13	TAS1	Я сознательно беру на себя больше рабочих задач, чем от меня требуется	I choose to take on additional tasks at work
14	TAS2	Я берусь за дополнительные задачи, даже без оплаты, если они помогут мне продвинуться в карьере	I take on extra tasks that contribute to my career even if I do not receive extra pay for them
15	TAS3	Я с воодушевлением берусь за новые обязанности, если они соответствуют моим карьерным интересам	Added work responsibilities excite me when they are relevant to my career interests

Scoring Key

Responses are scored on a 5–point scale ranging from 1 = Strongly disagree to 5 = Strongly agree. The score for each subscale and the overall career crafting scale is calculated as the arithmetic mean of the corresponding items.

Table A2

Scoring Key for the CCA-R

No.	Code	Item numbers
1	Changing Relational Boundaries (CHG)	1–4
2	Utilizing Relational Resources (RES)	5–8
3	Reflecting Positive Career Meaning (POS)	9–12
4	Expanding Task Boundaries (TAS)	13–15
5	Career Crafting (total)	1–15

Appendix B

Descriptive Statistics for the CCA-R

Table B1

Descriptive Statistics for CCA-R

No.	Scale	M	SD	Median	Skewness	Kurtosis
1	CHG	3.57	.86	3.75	-.78	.73
2	RES	3.16	.91	3.25	-.32	-.43
3	POS	3.69	.74	3.75	-.72	1.01
4	TAS	3.19	.89	3.33	-.41	-.15
5	total	3.42	.71	3.53	-.56	.44

Note. Scores were computed as mean values; possible and observed range: 1–5. SE skewness = .074 and SE kurtosis = .147 for all scales.

Appendix C

Exploratory Factor Analysis of the CCA-R

Four–Factor Structure

The four–factor structure accounted for 61% of the variance: 25% for Changing Relational Boundaries, 14% for Reflecting Positive Career Meaning, 13% for Expanding Task Boundaries, and 8% for Utilizing Relational Resources. Factor loadings ranged from low to high (.31–.87), and three items were poorly differentiated (RES2, RES3, POS4). Fit indices: RMSEA = .049, TLI = .969, BIC = −170.27.

Table C1

Factor Loadings for the Four–Factor Model of the CCA-R

No.	Code	Changing Relational Boundaries	Utilizing Relational Resources	Reflecting Positive Career Meaning	Expanding Task Boundaries
1	CHG1	.66
2	CHG2	.81
3	CHG3	.85
4	CHG4	.86
5	RES1	.64
6	RES2	.38	.35
7	RES3	.45	.40
8	RES4		.64
9	POS1		.31	.56
10	POS2			.76
11	POS3			.87
12	POS4			.32	.36
13	TAS1				.72
14	TAS2				.83
15	TAS3				.56

Note. Only factor loadings > .30 are reported. Factors were extracted using maximum likelihood estimation and oblimin rotation.

Three–Factor Structure

The three-factor solution accounted for 57% of the variance: 31% corresponded to the social factor, which combined the loadings of Utilizing Relational Resources and Changing Relational Boundaries, 14% to Reflecting Positive Career Meaning, and 13% to Expanding Task Boundaries. Within this structure, only item POS4 behaved ambiguously, splitting its loadings between its intended factor and Expanding Task Boundaries. Fit indices: RMSEA = .074, TLI = .929, BIC = -2.33.

Table C2

Factor Loadings for the Three–Factor Model of the CCA-R

No.	Code	Relational Factor	Reflecting Positive Career Meaning	Expanding Task Boundaries
1	CHG1	.78
2	CHG2	.80
3	CHG3	.81
4	CHG4	.84
5	RES1	.74
6	RES2	.61
7	RES3	.70
8	RES4	.45
9	POS1		.48
10	POS2		.78
11	POS3		.87
12	POS4		.34	.33
13	TAS1			.73
14	TAS2			.80
15	TAS3			.51

Note. Only factor loadings > .30 are reported. Factors were extracted using maximum likelihood estimation and oblimin rotation.