Romanian Version of the Career Crafting Assessment: Psychometric Properties

Abstract

The notion of career is no longer understood as a progressive advancement within the same organization, but rather as a lifelong dynamic transition between multiple jobs. Lee et al. (2021) developed Career Crafting Assessment as a means of identifying the proactive behaviors that people engage in when developing their career paths. The aim of this study was to test the psychometric properties of the Romanian version of the scale. In Study 1 (N = 938), Confirmatory Factor Analysis supported the four-factor model of the scale. The results supported the measurement invariance for gender, but not for age. The internal consistency of the scale was adequate. In Study 2 (N = 344), we found significant positive correlations between career crafting and two similar constructs, namely job crafting and organizational career management, supporting the convergent validity of the scale. In Study 3 (N = 213), the criterion validity of the scale was supported by significant positive correlations between career crafting and four of its possible outcomes, namely performance, work engagement, perceived meaningfulness of work, and person-job fit. Our results are in line with the findings of Lee et al. (2021) and suggest that the Romanian version of the instrument is valid.

Keywords

career crafting psychometric properties scale validation Romania

Introduction

The labor market in Romania faces certain challenges compared to the rest of the European Union, with less than two thirds of people aged 15-64 being employed (Eurostat., 2022). Most of the unemployed are workers without a post-secondary degree or credential. The main jobs available for them include low-wage jobs in factories and construction, driving, security, and selling (European Employment Services Network, 2022). Although the Covid-19 pandemic had an initial negative impact on employment in Romania, existing estimates are optimistic and suggest a decrease in unemployment and an increase in job opportunities for 2023 (Davidescu et al., 2021). These new employment opportunities could motivate some Romanians to start a new career. However, the existing literature has only barely explored the actions that people take in order to make career changes. One of the reasons for the lack of studies investigating personal initiatives in career development is that there were no adequate psychological instruments to measure these types of behaviors. Lee et al. (2021) addressed this gap in research by creating the Career Crafting Assessment. This is a psychometric scale that allows researchers to clearly identify and measure a set of proactive behaviors that people engage in when trying to achieve congruence between their career path and their specific needs, interests or life goals (Lee et al., 2021).

Research shows that both the nature of jobs and the personal characteristics of workers change over time (De Vos et al., 2019; Jin & Rounds, 2012). Thus, career crafting behaviors can help individuals to adjust their career path to better meet their needs (Lee et al., 2021), potentially leading to a series of positive outcomes. However, career crafting is a newly introduced construct. Therefore, empirical information about its specific effects is lacking. A lot of the existing research has focused on the role of proactivity and proactive personality instead. Specifically, studies have shown that being proactive at work have positive effects on the employees’ well-being (De Vos et al., 2019), job performance (Bergeron et al., 2014; De Vos et al., 2019; Joo & Bennett III, 2018), and perception of their own career success (De Vos et al., 2019; Erdogan & Bauer, 2005; Fuller & Marler, 2009; Seibert et al., 1999). Career crafting behaviors are conceptualized as specific categories of proactive behaviors. Therefore, we argue that career crafting is likely to have similar outcomes to proactivity. Some initial support for this hypothesis was offered by Lee et al. (2021), who found a positive correlation between career crafting, work engagement, and subjective career success.

Considering the possible positive consequences of career crafting and the lack of instruments to measure these career-related initiatives, the purpose of the current study is to evaluate the psychometric properties of the Romanian version of Career Crafting Assessment. This study has a series of theoretical contributions. First, Lee et al. (2021) developed the Career Crafting Assessment on a sample of participants from both Canada and the USA. However, these two cultures might be fairly similar since they are both categorized as strongly individualistic according to Hofstede’s model (Hofstede, 2011). This raises the question of whether the concept of career crafting can also be applied to predominantly collectivist cultures, such as Romania (Hofstede, 2011). Second, given the fact that career crafting is a newly introduced concept, there are no studies investigating its cross-cultural validity. The validation of the instrument for different languages can allow the assessment of measurement invariance across cultures. Finally, the validation of the instrument for several countries allows comparisons related to the way people craft their career in different cultures. For example, it would be interesting to investigate any possible differences in the way that Romanians craft their careers compared to Americans and Canadians, and whether those differences are associated with the way people feel about their work and work environment.

Career Crafting Assessment

Career crafting is characterized as “a set of proactive behaviors that create or expand task, relational, and cognitive resources with the goal of supporting individuals’ efforts toward achieving congruence between their career and their needs, values, and interests” (Lee et al., 2021). Lee et al. (2021) consider that proactivity and congruence are two defining features of career crafting. More specifically, they argue that proactivity helps people to access external resources such as networks, mentoring programs, promotions, and other career opportunities (Lee et al., 2021). By contrast, congruence “guides an individual to resources within the self, such as their values, interests, and needs” (Lee et al., 2021).

The dimensions of career crafting were derived from the three domains of job crafting proposed by Wrzesniewski & Dutton (2001). These include job-level task crafting, job-level relational crafting, and job-level cognitive crafting (Wrzesniewski & Dutton, 2001). Task crafting refers to changing the number, scope, or type of job tasks. Relational crafting refers to changing the quantity or quality of interpersonal interactions that one has at work. Finally, cognitive crafting refers to changing the way someone perceives their job (e.g., as a set of discrete work tasks or as an integrated whole) (Wrzesniewski & Dutton, 2001).

In their study, Lee et al. (2021) have found four different dimensions of career crafting: changing relational boundaries, utilizing relational resources, reflecting positive career meaning, and expanding task boundaries. Changing relational boundaries and utilizing relational resources were conceptualized as two different manifestations of career-level relational crafting, which implies proactively searching for and connecting with people with similar career interests and values (Lee et al., 2021). While the first one refers to expanding one’s own career-relevant social network (e.g., “I make connections with people who share my career interests.”), the second one refers to the use of social relationships and interactions as a means of developing one’s own professional skills and career path (e.g., “I seek professional coaching from those whose careers I admire.”). Similarly, expanding task boundaries is considered to be a form of career-level task crafting, as it represents the extent to which the individual is actively trying to develop new skills and abilities that will help them become a better professional (e.g., “Added work responsibilities excite me when they are relevant to my career interests.”) (Lee et al., 2021). Finally, reflecting positive career meaning is defined as the cognitive dimension of career crafting, which refers to the extent to which individuals perceive their work as meaningful and frame their career as an essential part of their life (e.g., “I think about the ways in which my career positively impacts my life.”) (Lee et al., 2021). Career Crafting Assessment (Lee et al., 2021) comprises of four subscales that correspond to the four dimensions listed above. These dimensions were first identified through an exploratory factor analysis, and were later validated using a confirmatory factor analysis (Lee et al., 2021). The scale has 15 items in total. Each subscale has 4 items except for expanding task boundaries, which has only three items. All items are measured on a 7-point Likert scale, ranging from 1 - Does not describe me at all to 7 - Completely describes me.

The scale showed an overall high internal consistency (α = .93), with Cronbach’s α = .93, .88, .86, and .83 for changing relational boundaries, utilizing relational resources, reflecting positive career meaning, and expanding task boundaries, respectively. Furthermore, positive correlations between career crafting and several other variables, such as protean career orientation, career self-management, career exploration, organizational career management, and job crafting, indicate that Career Crafting Assessment possesses an adequate convergent validity (Lee et al., 2021). Finally, scores collected on the Career Crafting Assessment significantly predicted meaningful work, work engagement, and subjective career success in a previous study (Lee et al., 2021), which might indicate a good concurrent validity of the construct. Although career crafting has been frequently mentioned across studies, there has been little consensus about the way this construct should be defined or measured. To this date, the paper of Lee et al. (2021) is the only scientific article which attempts to clearly conceptualize career crafting and examine its correlates using a viable psychometric instrument. However, this instrument, namely Career Crafting Assessment, has only been validated in two countries, which also happen to possess very similar cultural attributes. Therefore, we concluded that career crafting literature is lacking in information about the validity of this measurement in different cultures and populations.

Urbach et al. (2021) suggested that individuals from individualistic and low power distance cultures are socialized to value independence and agency. In these cultures, employees are granted more autonomy and are expected to demonstrate personal initiative (Urbach et al., 2021). Conversely, individuals from collectivistic and high power distance cultures are socialized to value conformity and act according to their role in a social hierarchy (Urbach et al., 2021). In these cultures, followers are expected to prioritize organizational interests above their own interests. In addition, they are expected to comply with their leader’s decisions and diligently fulfil their tasks (Urbach et al., 2021). Taking these differences into account, Urbach et al. (2021) argues that people from individualistic and low power distance cultures are more likely to engage in proactive behaviours than people from collectivistic and high power distance cultures. In line with this, Hong et al. (2022) found a negative relationship between vertical collectivism (adherence to an overt social hierarchy and willingness to give up personal interests if required by an authority) and protean career attitudes. Career crafting is positively related with both proactivity and protean career orientation (Lee et al., 2021). Therefore, it is possible that cultural differences may also impact the propensity and nature of career crafting behaviours. However, Career Crafting Assessment has only been validated in Canada and the United States, which are individualistic and low power distance cultures (Hofstede, 2011). In an attempt to diminish this gap, we investigated career crafting in Romania, which is a collectivist and high power distance culture (Hofstede, 2011).

Lee et al. (2021) did not investigate the measurement invariance of the scale. However, existing studies found different career patterns across generations (e.g., the younger generations have greater job and organizational mobility, with Millennials having almost three times as many jobs as Boomers; Lyons et al., 2015). Although these differences could be due to career stage, life-cycle, and economic factors rather than the generation itself (Lyons et al., 2015; Macky et al., 2008), they could influence career crafting behaviours and, therefore, the way participants from different generations answer the questionnaire. Similar differences can be observed in relation to gender. For example, gender differences in competitiveness or motivations lead to different career decisions between men and women (Buser et al., 2014; Heiligers, 2012). These differences can also be expressed in the way people craft their careers. For example, due to higher competitiveness and stronger motivation for status, men may use their social network more than women to advance their careers (e.g. to meet people who can help them get promoted or to seek circumstances favourable for their career). Therefore, it is possible that there are gender differences in terms of responses to the scale. We intend to address this issue by evaluating the measurement invariance of the scale across gender and generations (Gen Z, Millennials, Gen X, and Boomers).

Research Objective

The general objective of the current research was to analyze the psychometric properties of the Romanian version of the Career Crafting Assessment. We achieved this goal through three studies. In the first study, we conducted a confirmatory factor analysis on a sample of Romanian participants, we investigated the measurement invariance of the scale across gender and generations, and we evaluated the internal consistency of the scale and the correlations between career crafting factors. In the second study, we tested the convergent validity of the scale by correlating career crafting scores with those obtained for similar constructs (organizational career management and job crafting). In the third study, we tested the criterion validity of the scale by correlating its scores with a series of plausible consequences (work engagement, meaningfulness of work, and person-job fit). We expect positive relationships between career crafting and all these outcomes.

Study 1. Confirmatory Factor Analysis and Measurement Invariance Method

Participants

Our data was collected from a convenience sample consisting of 1127 Romanian employees, who have been recruited using the snowball method. To increase the similarity between our sample and the one used in the initial scale development study, we selected participants based on eligibility criteria that are similar to those imposed by Lee et al. (2021). Therefore, all participants who did not meet the following criteria were excluded from the analysis: (1) must be at least 18 years old (the legal age for full-time employment in Romania); (2) must have at least 12 months of experience as a full time employee; (3) must be currently employed under an employment contract. In addition, freelancers and company owners have also been excluded from the final sample because their work is impossible to carry out without engaging in career crafting behaviors. The final sample consisted of 938 full-time employees, aged between 18 and 68 years old (M = 35.54, SD = 11.35). Participants had an average work tenure of M = 13.91 years (SD = 10.61), with the least experienced respondents having only 12 months of experience on the job and the most experienced ones having been active on the job market for 48 years. With men constituting only 26.2% (n = 246) of respondents, the vast majority of the sample was represented by women (73.6%, N = 690). Two participants preferred not to declare their gender (.2%). In regards to their field of work, 25.5% (n = 239) of employees were working in commercial services, 18.6% (n = 174) in education, 10.1% (n = 95) in health and social services, 9.1% (n = 95) in national defense, public administration and government, 8.1% (n = 76) in finance, banking and real estate, 7% (n = 66) trade and sales, 5.2% (n = 49) in the field of transport and communications, 4.5% (n = 42) in construction, 3.7% (n = 35) in the processing industry, 3.4% (n = 32) in art, sports and entertainment industries, 3.3% (n = 31) in the hospitality industry, 1.3% (n = 12) in agriculture, and .2% (n = 2) in the energy producing industry.

Procedure

Participants completed an online questionnaire. At the beginning of the questionnaire, participants were asked to answer a series of questions regarding demographic information. All participants gave their consent to participate in the study and explicitly agreed with the use of personal data for scientific purposes. The statistical analyses were conducted using SPSS Statistics Version 25 (2020) and Mplus version 7 (Muthén & Muthén, 1988–2012).

Instrument

The Romanian version of Career Crafting Assessment (Lee et al., 2021) was used, which measures four dimensions of career crafting: changing relational boundaries (e.g., “I make connections with people who share my career interests.”), utilizing relational resources (e.g., “I seek professional coaching from those whose careers I admire.”), reflecting positive career meaning (e.g., “I think about the ways in which my career positively impacts my life.”), and expanding task boundaries (e.g., “Added work responsibilities excite me when they are relevant to my career interests.”). The scale consists of 15 items (see Appendix 1) measured on a 7-point Likert scale, ranging from 1 - Does not describe me at all to 7 - Completely describes me. The same items and scoring system were used by Lee et al. (2021).

Translation Process

The items included in the questionnaire were translated from English to Romanian by a group of subject matter experts in the field of industrial-organizational psychology. The Romanian translation was carried out by some of the experts in the group. The Romanian items were then translated back into English by the rest of the experts in the group, without them having access to the original version of the scale. After that, the two English versions (the original version and the back translation) were compared and found to be equivalent.

Results

Confirmatory Factor Analysis

In order to test the factor structure of the construct, we conducted a series of confirmatory factor analyses in Mplus version 7 (Muthén & Muthén, 1988–2012). Specifically, we have investigated the fit between our data and three theoretical models - the four-factor model that we have previously described, a second-order model, and a three-factor model. These models were also tested by Lee et al. (2021). Following the recommendations offered by Jackson et al. (2009), the three models were compared based on the values of the following model fit indices: Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA). For CFI and TLI, values greater than .95 are considered to indicate a good model fit (Hu & Bentler, 1999), while values greater than .90 indicate an acceptable fit (Hu & Bentler, 1998). In regards to the RMSEA index, some authors have proposed that in order to have a good model fit, its values should not exceed .05 (Schumacker & Lomax, 2010) or .06 (Sun, 2005). However, other authors have argued that any value less than .08 of the RMSEA index suggests an acceptable fit (Browne & Cudeck, 1992) and values between .08 and .10 indicate a marginal fit (Fabrigar et al., 1999). Similarly, Hu and Bentler (1999) consider that an acceptable model fit is indicated by a SRMR index of a value no greater than .08.

Taking into account all of the above, a first analysis of the statistical results indicated an overall poor fit of the data with the four-factor theoretical model (CFI = .91, TLI = .89, SRMR = .06, RMSEA = .09; RMSEA 90% = [.08, .10]). Following the recommendations of multiple researchers (Byrne, 2001; DiStefano & Hess, 2005; Jackson et al., 2009), we also analyzed the values of modification indices in order to gain a deeper understanding of the model inconsistencies. Based on this analysis, it was found that there is a residual covariance between the measurement errors of two items. Given that the highest residual covariance was observed between items 7 and 8 (both from the second factor), we introduced this covariance into the statistical model and conducted the confirmatory factor analysis procedure once again. The results showed significant improvements in all model fit indices (CFI = .94, TLI = .93, SRMR = .05, RMSEA = .08; RMSEA 90% = [.07, .08]). The standardized parameter estimates of this CFA model are presented in Table 1. While analyzing the phrasing of the two items, we noticed that they are similar in meaning. Specifically, item 7 states “I ask others to introduce me to people who can positively influence my career”. Similarly, item eight states “I ask others for feedback on my career development”. It is important to note that the original meaning of the items was not altered when translating from English to Romanian. Therefore, it is unlikely that the similarity between items was a result of language differences. Rather, we suggest that paired items might have a shared contribution to another construct besides career crafting. Specifically, offering feedback is one of the most common ways in which someone could have a positive influence over somebody else’s career. Thus, it is possible that participants interpreted item 7 as asking others to introduce them to people who can give them valuable feedback. In turn, this may have determined some of the participants to give almost identical answers to these particular questions, hence producing a systematic error of measurement. If correct, the reasoning above may explain the residual covariance between the two items. Thus, we thought it was justified to include the covariance in the statistical model. Another strategy for improving the model fit could be deleting one of the items. However, the items differ enough from one another to not be considered redundant. If we would exclude one of them, we would fail to measure the full spectrum of career crafting behaviors. Because of this, we chose not to dispose of any items. However, Lee et al. (2021) do not provide any information regarding the residual covariance between items. Therefore, we cannot assess whether our results differ from theirs in this regard.

Table 1.

CFA Standardized Parameter Estimates for the Romanian Version of Career Crafting Assessment (N = 938).

	Changing relational boundaries	Utilizing relational resources	Reflecting positive career meaning	Expanding task boundaries
1. I make an effort to get to know people whose career I admire	.727
2. I make connections with people who share my career interests	.808
3. I make connections with people who have the skills I want to develop in my career	.849
4. I build contacts with people in areas where I would like to work	.698
5. I seek professional coaching from those whose careers I admire		.735
6. I attend events that will help me explore different career paths		.578
7. I ask others to introduce me to people who can positively influence my career		.493
8. I ask others for feedback on my career development		.487
9. I think about the ways in which my career positively impacts my life			.785
10. I remind myself that my career has personal significance			.858
11. I reflect on the role my career has for my overall well-being			.781
12. I look at a career as a means of expressing myself			.688
13. I choose to take on additional tasks at work				.830
14. I take on extra tasks that contribute to my career even if I do not receive extra pay for them				.910
15. Added work responsibilities excite me when they are relevant to my career interests				.798

The second-order model resulted from adding a career crafting global factor loaded by the four career crafting dimensions. The results of the confirmatory analysis were similar to those obtained for the four-factor model (CFI = .94, TLI = .93, SRMR = .06, RMSEA = .08; RMSEA 90% = [.07, .08]). The three-factor model was obtained by combining the first two factors (changing relational boundaries and utilizing relational resources) into one. The results of the confirmatory factor analysis indicated a slightly weaker model fit (CFI = .93, TLI = .92, SRMR = .07, RMSEA = .08; RMSEA 90% = [.07, .09]). The residual covariance between items 7 and eight was declared both for the second-order model and for the three-factor model. Given the results obtained from the confirmatory factor analysis, we considered that the structure of our data was most adequately described by the four-factor model, which is why we applied all of the following analyses to this particular model. CFA results are presented in Table 2.

Table 2.

Confirmatory factor analysis of the Romanian version of Career Crafting Assessment (N = 938).

	χ²	df	RMSEA	CFI	TLI	SRMR
4-Factor model	758.48	84	.09	.91	.89	.06
4-Factor model (covariance between item 7 and item 8)	519.67	83	.08	.94	.93	.05
Second-order model (covariance between item 7 and item 8)	553.94	85	.08	.94	.93	.06
3-Factor model (covariance between item 7 and item 8)	596.65	86	.08	.93	.92	.07

Measurement Invariance

We tested the measurement equivalence of the scale across gender and generations by using a multi-group CFA in order to evaluate three types of invariance: (1) configural invariance, which means that the scale has the same factorial structure for all groups (Schmitt & Kuljanin, 2008) and it is tested by simultaneously declaring the same factorial structure for all groups (Cheung & Rensvold, 2000); (2) metric invariance, which means that the items load the factors in the same way for all groups and is evaluated by comparing an unconstrained model with a model in which all loadings are declared equal for all groups (Schmitt & Kuljanin, 2008), and (3) scalar invariance, which refers to the similarity of the regression equations’ intercepts of the observed variables on the factors for all groups (Schmitt & Kuljanin, 2008). According to the recommendations in the literature (Chen, 2007; Little, 1997), metric invariance is supported when ∆CFI <.005, ∆RMSEA <.010, ∆SRMR <.025, and the change in ꭓ² is non-significant. The chi-square difference was evaluated through the Satorra-Bentler scaled chi-square (Satorra & Bentler, 2010), computed on https://www.thestatisticalmind.com/calculators/SBChiSquareDifferenceTest.htm. Scalar invariance is supported when ∆CFI <.005, ∆RMSEA <.010, and ∆SRMR <.005 (Chen, 2007).

First, we tested the model fit for each group separately. The women’s group was composed of 690 participants and the men’s group of 246 participants. Two people did not declare their gender. Both the women’s group (CFI = .94, TLI = .92, SRMR = .06, RMSEA = .08; RMSEA 90% = [.07, .09]) and the men’s group (CFI = .94, TLI = .92, SRMR = .06, RMSEA = .08; RMSEA 90% = [.07, .09]) presented good fit indices. Then, measurement invariance across gender was analyzed. Women’s group was used as the reference group in all the invariance models. The results support the measurement invariance across gender (see Table 3).

Table 3.

Measurement Invariance Across Gender and Generations.

	χ²	df	RMSEA	CFI	TLI	SRMR	∆ χ²	∆RMSEA	∆CFI	∆SRMR
Gender (N = 936)
Configural invariance	459.00	166	.061	.940	.924	.056	-	-	-	-
Metric invariance	480.12	181	.059	.939	.929	.063	21.12^ns	−.002	−.001	.008
Scalar invariance	516.98	188	.061	.933	.925	.066	36.56^ns	.002	−.006	.003
Generations (N = 919)
Configural invariance	560.63	249	.064	.937	.921	.061	-	-	-	-
Metric invariance	605.40	279	.045	.934	.926	.093	44.77^ns	−.019	−.003	.032
Scalar invariance	670.00	293	.065	.924	.919	.077	64.60^ns	.020	−.010	−.016

Note: χ² = chi-square; df = degrees of freedom; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; TLI = Tucker Lewis Index; SRMR = Standardized Root Mean Squared; ∆RMSEA = Root Mean Square Error of Approximation difference; ∆CFI = Comparative Fit Index difference; ∆SRMR = Standardized Root Mean Squared difference; ns = not significant.

To test measurement invariance across generations, four groups were constructed: Gen Z (between 18 and 25 years; n = 283), Millennials (between 26 and 41 years; n = 326), Gen X (between 42 and 57 years; n = 310), and Boomers (more than 58 years; n = 19). Given that groups included in the measurement invariance testing should include more than 200 participants (Meade & Kroustalis, 2006), the group of boomers was excluded from the analysis. Both the Gen Z’s group (CFI = .94, TLI = .92, SRMR = .07, RMSEA = .07; RMSEA 90% = [.06, .09]) and the Millennials’ group (CFI = .94, TLI = .92, SRMR = .05, RMSEA = .08; RMSEA 90% = [.07, .09]) presented good fit indices. The Gen X’s group (CFI = .93, TLI = .91, SRMR = .06, RMSEA = .09; RMSEA 90% = [.08, .10]) presented a marginal fit. Based on these results, measurement invariance was tested. Gen Z’s group was used as the reference group in all the invariance models. Results do not support the measurement invariance across generations (see Table 3). Based on the model modification indices, the most obvious difference was the way in which Item 15 (“Added work responsibilities excite me when they are relevant to my career interests.”) loaded more factors than the main one (i.e., expanding task boundaries). For Millennials, Item 15 also loaded on utilizing relational resources and reflecting positive career meaning. For Gen X, Item 15 loaded on all the factors. A possible explanation for these results is the fact that, as they gain work experience, the employees tend to become unresponsive to the task of their jobs due to habit and routine (Orpen, 1984). Therefore, for experienced employees, new tasks that excite them can also have a positive effect on the meaning they attribute to work and can stimulate them to use their professional social resources.

Internal Consistency and Correlations between Factors

We used the Cronbach’s Alpha coefficient to examine the internal consistency of each subscale, as well as the internal consistency of the instrument as a whole. Results showed similar values to the ones obtained by Lee et al. (2021) (see Table 2). All Cronbach’s Alpha coefficients fall within the range of values conventionally considered as appropriate (Nunnally, 1978; Tavakol & Dennick, 2011; Vaske et al., 2017). In addition, we identified high correlations between the four components of career crafting and between the four factors and the general score (see Table 4).

Table 4.

Descriptive Statistics, Reliabilities, and Correlations Among Factors (N = 938).

	M	SD	1	2	3	4	5
1. Changing relational boundaries	5.78	1.19	(.85)
2. Utilizing relational resources	4.87	1.36	.61***	(.74)
3. Reflecting positive career meaning	5.64	1.22	.55***	.62***	(.85)
4. Expanding task boundaries	5.41	1.52	.42***	.44***	.50***	(.88)
5. Career crafting	5.42	1.06	.79***	.83***	.82***	.77***	(.91)

Note: Cronbach’s alpha reliabilities are in parentheses on the diagonal, ***p < .001.

Study 2. Convergent Validity Method

Participants

For this study, we used a convenience sample. Participants were recruited using the snowball method. The eligibility criteria were the same as in Study 1, except that one additional criterion was introduced. The instruments that we used included some questions about the employee’s relationship with their direct supervisor as well as their organization. Therefore, it was necessary for each participant to be part of an organization and have a supervisor. Of the total number of eligible participants (N = 344), 73.3% were women (n = 252) and 26.7% were men (n = 92). The age of participants ranged from 19 to 68 years, with a mean of 36.6 years (SD = 11.66) and their work experience varied between 1 and 48 years, with an average of 14.91 years (SD = 10.81). 93% (n = 320) of participants were full-time employees, while 7% worked part-time jobs. Only 16% of respondents were managers, as the vast majority of our sample consisted of people without managerial positions (84%, n = 127). From the total number of participants, 36.9% held a bachelor degree (n = 127), 34.9% held a master’s degree (n = 120), 24.7% held a high school diploma (n = 85), 3.2% held a doctoral degree (n = 11) and .3% (n = 1) held a post-secondary school diploma. 30.8% of the participants were employed in the field of commercial services (n = 106), 11.9% in education (n = 41), 11.6% in finance, banking and real estate (n = 40), 10.2% in health and social services (n = 35), 9.6% in national defense, public administration and government (n = 33), 5.5% in construction (n = 19), 5.2% in trade and sales (n = 18), 3.8% in art, sports and entertainment industries (n = 13), 3.5% in the field of transport and communications (n = 12), 3.2% in the processing industry (n = 11), 2.3% in the hospitality industry (n = 8) and .3% in the energy industry (n = 1).

Procedure

Participants completed an online questionnaire created in Google Forms. At the beginning of the questionnaire, we collected demographic information about each participant. All participants signed an informed consent prior to the completion of the study. To analyze convergent validity, we examined the Pearson correlation coefficients between career crafting and two other variables, namely job crafting and organizational career management. Job crafting refers to those self-initiated behaviors that employees engage in with the purpose of better aligning their jobs with their own work preferences, motives, and interests (Tims et al., 2012). While job crafting behaviors only affect the way in which a person fulfills a specific role in an organization, career crafting behaviors shape the entire career development process of an individual. Given the similarities between career crafting and job crafting (De Vos et al., 2019), it is expected that there is at least a moderate correlation between the two. Indeed, Lee et al. (2021) have found moderate to strong positive correlations between career crafting and all facets of job crafting, except for decreasing hindering job demands. Unlike career crafting, which refers to those behaviors that are initiated by the employees, organizational career management describes the measures taken by the organization in order to aid its employees’ career development (De Vos et al., 2009). Training sessions, mentoring programs or individual learning and development plans are a few examples of common organizational career management practices (De Vos et al., 2009). These types of organizational practices could stimulate employees to search for career development opportunities inside their current organization. For instance, an individual may ask their assigned mentor or buddy for feedback regarding their career development (utilizing relational resources), or make connections with colleagues from a different department in order to learn new skills (changing relational boundaries). Lee et al. (2021) obtained moderate to strong correlations between career crafting and organizational career management.

Instruments

Career crafting was measured with the instrument described above.

Organizational career management was measured with the Organizational Career Management Scale (Sturges et al., 2002), which includes 10 items on a 5-point Likert scale, ranging from 1 - Completely disagree to 5 - Completely agree. The items are grouped into two factors: formal help (e.g., “I have been given training to help develop my career.”) and informal help (e.g., “I have been given a mentor to help my career development.”). This scale was translated using the same procedure as for the translation of Career Crafting Assessment.

Job crafting was measured with the Romanian version of the Job Crafting Scale (Oprea & Ștefan, 2019; Tims et al., 2012), which measures four dimensions: increasing job resources (e.g., “I try to learn new things at work.”), decreasing hindering job demands (e.g., “I make sure that my work is mentally less intense.”), increasing challenging job demands (e.g., “When an interesting project comes along, I offer myself proactively as project co-worker.”), increasing social job resources (e.g., “I ask whether my supervisor is satisfied with my work.”). The scale consists of 21 items measured on a 5-point Likert scale ranging from 1 - Never to 5 - Often. However, we did not include the dimension of decreasing hindering job demands in the questionnaire because it was not significantly correlated with career crafting in previous studies (Lee et al., 2021).

Results

We found significant positive correlations between all the career crafting dimensions and both job crafting and organizational career management (see Table 5). Similarly, to what Lee et al. (2021) have found in their study, increasing structural job resources and increasing challenging job demands showed moderate to strong positive correlations with all four dimensions of career crafting (see Table 5). Out of the three dimensions of job crafting that we introduced in our analysis, increasing challenging job demands was the most strongly associated with career crafting. Specifically, increasing challenging job demands was strongly correlated with expanding task boundaries (r = .69, p < .001) and moderately correlated with challenging relational boundaries (r = .38, p < .001), utilizing relational resources (r = .48 p < .001), and reflecting positive career meaning (r = .39, p < .001). However, the data collected from our sample revealed only low to moderate correlations between career crafting and increasing social job resources, which comes into contradiction with the findings from previous studies (Lee et al., 2021).

Table 5.

Descriptive Statistics, Reliabilities, and Correlations between Variables (N = 344).

	M	SD	1	2	3	4	5	6	7	8	9	10
1. Changing relational boundaries	5.68	1.20	(.86)
2. Utilizing relational resources	4.64	1.38	.59***	(.71)
3. Reflecting positive career meaning	5.72	1.25	.56***	.63***	(.86)
4. Expanding task boundaries	5.38	1.43	.39***	.37***	.44***	(.86)
5. Formal help	3.40	1.02	.25***	.32***	.37***	.28***	(.86)
6. Informal help	3.35	1.10	.35***	.43***	.38***	.23***	.68***	(.86)
7. Organizational career management	3.38	.97	.32***	.39***	.40***	.28***	.94***	.88***	(.90)
8. Increasing structural job resources	4.43	.58	.38***	.31***	.38***	.38***	.27***	.21***	.26***	(.80)
9. Increasing challenging job demands	3.51	.85	.38***	48***	.39***	.69***	.27***	.24***	.28***	.49***	(.80)
10. Increasing social job resources	3.06	.96	.26***	.50***	.39***	.28***	.61***	.53***	.62***	.21***	.34***	(.80)

Note: Cronbach’s alpha reliabilities are in parentheses on the diagonal, ***p < .01.

All four dimensions of career crafting showed moderate positive correlations with organizational career management, with the exception of expanding task boundaries, which was weakly correlated with both formal help (r = .28, p < .001) and informal help (r = .23, p < .001), and changing relational boundaries, which was weakly correlated with formal help (r = .25, p < .001). These results are slightly different from those of Lee et al. (2021) who obtained moderate to strong correlations between the two constructs.

Study 3. Criterion Validity Method

Participants

We collected data from a convenience sample of N = 213 Romanian employees, with up to 46 years of working experience and a minimum of 12 months of experience in a full time working regime (M = 14.17, SD = 10.21). Initially, 226 respondents completed the study, but 13 of them were excluded from the final analysis because they did not meet the eligibility criteria that have been previously described. In regards to the gender of participants, the sample consisted of 80.3% females (n = 171) and 19.2% males (n = 41). The remaining .5% of the sample was accounted for by a single participant who preferred not to disclose the gender. The age of participants ranged from 19 to 67 years, with a mean of 35.99 years (SD = 11.03). Of the total sample, 44.6% of participants were employed in the field of education (n = 95), 13.1% in commercial services (n = 28), 9.4% in health and social services (n = 20), 7% in trade and sales (n = 15), 6.1% in finance, banking and real estate (n = 13), 5.6% in the field of transport and communication (n = 12), 3.3% in national defense, public administration and government (n = 7), 3.3% in construction (n = 7), 2.8% processing industry (n = 2.8%), 2.3% in art, sports and entertainment industries (n = 5), 1.9% in the hospitality industry (n = 4) and .5% agriculture (n = 1).

Procedure

All participants have formally consented with the processing of their personal information before completing the study. Respondents answered all of the items through an online questionnaire which was created in Google Forms and distributed on social media platforms. The research design was cross-sectional, as responses to all psychometric scales were collected one time only. In order to test criterion validity, we began by analyzing the relationship between career crafting and four of its possible outcomes: work engagement, perceived meaningfulness of work, person-job fit, and job performance. First of all, work engagement and perceived meaningfulness of work were chosen because previous research has supported their link with career crafting (De Vos et al., 2019; Kordbacheh et al., 2014; Van Wingerden & Van der Stoep, 2018). Furthermore, as career crafting behaviors are supposedly aimed at improving the congruence between the characteristics of work and those of the worker (Lee et al., 2021), we decided to introduce person-job fit as a third criteria. Finally, we chose job performance based on the job demands-resources model (Bakker & Demerouti, 2014), which states that employees perform better at work when they engage in behaviors that maximize their resources.

Instruments

Career crafting was measured with the instrument described above.

Work engagement was measured using the Romanian adaptation (Vîrgă et al., 2009) of the Utrecht Work Engagement Scale – Short Version (UWES-9) (Schaufeli et al., 2006). The scale identifies three dimensions of work engagement, namely vigor (e.g. “At my work, I feel bursting with energy.”), absorption (e.g. “I get carried away when I am working.”), and dedication (e.g. “I am enthusiastic about my job.”). It consists of nine items measured of a 7-point Likert scale ranging from 0 – Never to 6 – Always/Every day.

Meaningfulness of work was measured using The Work and Meaning Inventory (Steger et al., 2012). The scale consists of 10 items measured on a 5-point Likert scale, ranging from 1 – Absolutely untrue to 5 – Absolutely true. The items are grouped into three factors: positive meaning (e.g. “I have found a meaningful career.”), meaning making through work (e.g. “My work helps me better understand myself.”), and greater good motivations (e.g. “The work I do serves a greater purpose.”). The Romanian version of the scale had adequate psychometric properties in previous studies (Oprea et al., 2022).

Person-job fit was measured using The Person–Job Fit Scale, a dimension extracted from The Perceived Person–Environment Fit Scale (Chuang et al., 2016). This subscale consists of only 4 items (e.g. “How would you describe the match between your professional skills, knowledge, and abilities and those required by the job?”), measured on a 7-point Likert scale ranging from 1 – No fit to 7 – Total fit. This scale was translated using the same procedure as for the translation of Career Crafting Assessment.

Job performance was assessed using the Goodman and Svyantek’s Performance Scale (Goodman & Svyantek, 1999). The instrument evaluates both task (e.g. “You plan and organize to achieve objectives of the job and meet deadlines.”) and contextual performance (e.g. “You assist your colleagues with their duties.”) and is comprised of a total of 16 items measured on a 4-points Likert scale (1 – Strongly disagree, 4 – Strongly agree). This scale had adequate psychometric properties in previous studies on Romanian employees (Bădoiu & Oprea, 2018).

Results

Correlations Between Variables

The four dimensions of career crafting showed moderate to strong positive correlations with task performance, contextual performance, and work engagement and weak to moderate positive correlations with perceived meaningfulness of work and person-job fit. The values of the correlation coefficients ranged from .23 to .62. All correlations were significant at p < .001 (see Table 6). These results are similar to findings from previous studies (Bergeron et al., 2014; De Vos et al., 2019; Lee et al., 2021).

Table 6.

Descriptive Statistics, Reliabilities, and Correlations between Variables (N = 213).

	M	SD	1	2	3	4	5	6	7	8	9
1. Changing relational boundaries	5.96	.98	(.79)
2. Utilizing relational resources	5.07	1.28	.61***	(.71)
3. Reflecting positive career meaning	5.76	1.17	.55***	.58***	(.87)
4. Expanding task boundaries	5.54	1.43	.49***	.50***	.64***	(.87)
5. Task performance	3.51	.43	.34***	.32***	.37***	.55***	(.86)
6. Contextual performance	3.30	.53	.34***	.44***	.40***	.62***	.64***	(.84)
7. Work engagement	4.88	1.01	.42***	.34***	.47***	.42***	.45***	.45***	(.93)
8. Meaningfulness of work	4.18	.76	.40***	.36***	.42***	.28***	.28***	.31***	.69***	(.90)
9. Person-job fit	5.93	.95	.36***	.26***	.27***	.23***	.32***	.29***	.67***	.66***	(.85)

Note: Cronbach’s alpha reliabilities are in parentheses on the diagonal, ***p < .001.

Discussions

Given the changing nature of work (Lytle et al., 2015), the notion of career is no longer understood as a progressive hierarchical advancement within the same organization, but rather as a lifelong dynamic transition between multiple jobs, occupations, positions and organizations. Thus, Career Crafting Assessment was developed as a means of identifying those proactive behaviors that people engage in when trying to implement meaningful career changes. The aim of this study was to test the psychometric properties of the Romanian version of the Career Crafting Assessment scale. Specifically, we investigated its factor structure, internal consistency, convergent and criterion validity, and measurement equivalence across different genders and age-groups.

Conclusions Regarding the Psychometric Properties of the Scale

Overall, the results indicate adequate psychometric properties for the Romanian version of Career Crafting Assessment. Firstly, results from the confirmatory factor analysis indicated a good fit between our data and the four-factor model proposed by Lee et al. (2021). Therefore, the structure of the Romanian version of Career Crafting Assessment appears to be similar to the one proposed by the authors of the instrument. Secondly, measurement invariance across genders was supported by data. This is an important finding, because it suggests that potential score differences between groups are most likely caused by actual gender differences, rather than the structure of the scale itself. However, measurement invariance across age groups was not supported and the data collected from Gen X participants presented only a marginal fit with the theoretical model. We propose two mechanisms that could potentially explain the lack of measurement invariance across age-groups: (1) as people get older and approach retirement, they become less focused on work and more concerned with fulfilling their roles as parents, grandparents or citizens, thus investing more time in non-career related activities (Cumming et al., 1960); (Super, 1980); (2) it is possible that more senior employees have already reached the peak of their careers, thus achieving the highest level of congruence possible and not needing to engage in career crafting behaviors anymore (Kanfer & Ackerman, 2004). Taking these into consideration, it is possible that the instrument does not maintain the same psychometric properties for all generations.

Thirdly, the instrument presents a good internal consistency, showing high values of the Cronbach’s Alpha coefficients for each subscale, as well as the entire instrument as a whole. Thus, Career Crafting Assessment is highly reliable and adequate for use in future research (Vaske et al., 2017). Finally, our results supported both the convergent validity and the criterion validity of the scale. In support of convergent validity, we found significant positive correlations between career crafting and two similar constructs, namely job crafting and organizational career management. However, as the strength of these correlations was predominantly moderate, we can infer that career crafting is a distinct construct which should not be confused with similar variables. Furthermore, criterion validity was supported by significant positive correlations between career crafting and four of its hypothetical outcomes, namely performance, work engagement, perceived meaningfulness of work, and person-job fit. To conclude, the psychometric properties of the Romanian version of Career Crafting Assessment are similar to those of its American counterpart. Thus, our results are in line with the findings of Lee et al. (2021) and suggest that the Romanian version of the instrument is valid.

Limitations and Future Research Directions

Although it provides a fairly comprehensive analysis of the characteristics of the scale, our study presents a number of limitations. First of all, our research design was cross-sectional, which means that it is impossible to draw any conclusions about the reliability of our results over time. Therefore, future studies could rely on a longitudinal approach in order to enable the analysis of test-retest reliability, as well as the predictive validity of the scale. In regards to predictive validity, we propose that constructs such as job performance and perceived career success are some of the outcomes that could be studied in the near future, as their positive associations with career crafting has already been suggested by other authors (Bergeron et al., 2014; De Vos et al., 2019).

Second of all, we have examined only a limited number of relationships between career crafting and other variables. In order to further test the assumption that career crafting leads to positive changes at work, the effect of career crafting on variables such as job satisfaction and overall employee well-being could be examined. In addition, future studies could analyze more complex models in order to understand the explanatory mechanisms through which career crafting may be related to other constructs and to identify moderators in the relationship between career crafting and its outcomes.

A third limit of our study concerns the exclusion of freelances and entrepreneurs from the final sample. By default, these people are more likely to have protean careers, and therefore they have an intrinsically higher need to rely on career crafting behaviors to complete their work. Future research could investigate whether career crafting behaviors vary significantly in either type or frequency, depending on the nature of employment. We have identified two other important directions for future research. Further investigations could be made in order to assess the efficacy of career crafting training programs. This could be achieved by following the methodology already proposed by Van Leeuwen et al. (2021). Such studies would be helpful in understanding the extent to which career crafting behaviors can be taught and learned within an organized framework and how these trainings may contribute to career counseling practices. Finally, in order to expand the existing body of knowledge on career crafting, future studies could focus on investigating the psychometric properties of Career Crafting Assessment in different cultures, such as those in Asia or Africa.

Practical Implications

The current research has a series of practical contributions. First, the Career Crafting Assessment can be used to measure the career growth strategies of Romanian employees. Understanding what types of actions people need to make in order to achieve congruence between their needs and their career path is crucial in guiding them towards developing a more fulfilling professional life. Thus, Career Crafting Assessment results could be used to improve both coaching and vocational counseling services in Romania. Second, the instrument can be used to test the effectiveness of career crafting interventions. Organizational psychologists and HR professionals could implement career crafting interventions not only to increase employees’ job satisfaction, but to also stimulate them to become more productive and engaged in their work (Lee et al., 2021; Van Wingerden & Van der Stoep, 2018; Van Leeuwen et al., 2021). Finally, measuring career crafting behaviors may be relevant in employee retention strategies. Better career growth opportunities might be a great incentive for high performing employees to remain within the company, thus preventing turnover caused by employees feeling like their current job no longer meets their needs (Joo & Park, 2010; Shuck et al., 2014; Stahl et al., 2009).

Romania was a communist country until 1989. In such a regime, which was based on a planned economy, jobs were assigned by the state to citizens. Job search or career development skills were not relevant (Andronic & Andronic, 2011). After the fall of communism and the transition to capitalism, changes in the labor market led to the emergence of career counseling services, but these services are largely limited to workforce entry, developing skills such as résumé writing and job search, and assessment of vocational interests (Andronic & Andronic, 2011; Hartung, 2005). Considering this context, it is possible that Romanians are not used to having career-related initiatives. By integrating the concept of career crafting, career counseling specialists can help Romanian employees to develop a proactive approach to their career, teaching them to use social resources in order to develop professionally, to seek challenging tasks, and to reflect on the positive aspects of their careers.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by The Association of Industrial and Organizational Psychology in Romania (APIO) [Asociatia de Psihologie Industriala si Organizationala].

ORCID iD

Bogdan Oprea

Appendix

Romanian version of Career Crafting Assessment

Nr	Item
Modificarea granițelor relaționale
1	Încerc să cunosc oameni cărora le admir cariera
2	Creez legături cu oameni care au aceleași interese de carieră ca și mine
3	Creez legături cu oameni care au competențe pe care vreau să mi le dezvolt în cariera mea
4	Creez legături cu oameni din domenii în care mi-ar plăcea să lucrez
Utilizarea resurselor relaționale
5	Caut îndrumare profesională de la oameni cărora le admir cariera
6	Particip la evenimente care mă ajută să explorez diferite direcții de carieră
7	Le cer celorlalți să mă prezinte oamenilor care ar putea avea o influență pozitivă asupra carierei mele
8	Le cer celorlalți feedback cu privire la dezvoltarea carierei mele
Reflectarea la însemnătatea pozitivă a carierei
9	Mă gândesc la modurile în care cariera îmi influențează pozitiv viața
10	Obișnuiesc să îmi reamintesc de însemnătatea personală pe care o are cariera mea
11	Obișnuiesc să reflect asupra rolului pe care îl are cariera mea asupra stării mele de bine
12	Mă raportez la cariera mea ca la un mijloc de auto-exprimare
Extinderea limitelor sarcinilor de lucru
13	Aleg să îmi asum sarcini suplimentare la locul de muncă
14	Îmi iau sarcini suplimentare dacă acestea contribuie la cariera mea, chiar dacă nu sunt plătit în plus
15	Mă entuziasmează responsabilitățile suplimentare atunci când acestea sunt relevante pentru interesele mele de carieră

References

Andronic

A. O.

Andronic

R. L.

(2011). Career counselling in Romania – impact on educational actors. Procedia-Social and Behavioral Sciences, 30, 1857–1861. https://doi.org/10.1016/j.sbspro.2011.10.360

Bădoiu

Oprea

(2018). The work-related flow (WOLF) inventory: Romanian adaptation. Psihologia Resurselor Umane, 16(2), 94–106. https://10.24837/pru.2018.2.489

Bakker

A. B.

Demerouti

(2014). Job demands–resources theory. Wellbeing: A complete reference guide (pp. 1–28).

Bergeron

D. M.

Schroeder

T. D.

Martinez

H. A.

(2014). Proactive personality at work: Seeing more to do and doing more? Journal of Business and Psychology, 29(1), 71–86. https://doi.org/10.1007/s10869-013-9298-5

Browne

M. W.

Cudeck

(1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005

Buser

Niederle

Oosterbeek

(2014). Gender, competitiveness, and career choices. The Quarterly Journal of Economics, 129(3), 1409–1447. https://doi.org/10.1093/qje/qju009

Byrne

B. M.

(2001). Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. International Journal of Testing, 1(1), 55–86. https://doi.org/10.1207/S15327574IJT0101_4

Chen

F. F.

(2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://10.1080/10705510701301834

Cheung

G. W.

Rensvold

R. B.

(2000). Assessing extreme and acquiescence response sets in cross-cultural research using structural equations modeling. Journal of Cross-Cultural Psychology, 31(2), 187–212. https://10.1177/0022022100031002003

10.

Chuang

Shen

C. T.

Judge

T. A.

(2016). Development of a multidimensional instrument of person–environment fit: The perceived person–environment fit scale (PPEFS). Applied Psychology, 65(1), 66–98. https://doi.org/10.1111/apps.12036

11.

Cumming

Dean

L. R.

Newell

D. S.

McCaffrey

(1960). Disengagement: A tentative theory of aging. Sociometry, 23, 23–25. https://doi.org/10.2307/2786135

12.

Davidescu

A. A.

Apostu

S. A.

Stoica

L. A.

(2021). Socioeconomic effects of COVID-19 pandemic: Exploring uncertainty in the forecast of the Romanian unemployment rate for the period 2020–2023. Sustainability, 13(13), 7078. https://doi.org/10.3390/su13137078

13.

De Vos

Akkermans

Van Der Heijden

B. I. J. M.

(2019). From occupational choice to career crafting (pp. 128–142). The Routledge companion to career studies. https://doi.org/10.1037/xge0000533

14.

De Vos

Dewettinck

Buyens

(2009). The professional career on the right track: A study on the interaction between career self-management and organizational career management in explaining employee outcomes. European Journal of Work and Organizational Psychology, 18(1), 55–80. https://doi.org/10.1080/13594320801966257

15.

DiStefano

Hess

(2005). Using confirmatory factor analysis for construct validation: An empirical review. Journal of Psychoeducational Assessment, 23(3), 225–241. https://doi.org/10.1177/073428290502300303

16.

Erdogan

Bauer

T. N.

(2005). Enhancing career benefits of employee proactive personality: The role of fit with jobs and organizations. Personnel Psychology, 58(4), 859–891. https://doi.org/10.1111/j.1744-6570.2005.00772.x

17.

Eurostat. (2022, January). Key figures on the changes to the labour market. Eurostat. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Key_figures_on_the_impact_of_the_COVID-19_crisis_on_the_labour_market&oldid=571760#Disparate_increase_in_employment.

18.

European Employment Services Network (2022, January). Labour market information: Romania. European Employment Services Network. https://eures.ec.europa.eu/living-and-working/labour-market-information/labour-market-information-romania_en

19.

Fabrigar

L. R.

Wegener

D. T.

MacCallum

R. C.

Strahan

E. J.

(1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272

20.

Fuller

B.,

Jr. Marler

L. E.

(2009). Change driven by nature: A meta-analytic review of the proactive personality literature. Journal of Vocational Behavior, 75(3), 329–345. https://doi.org/10.1016/j.jvb.2009.05.008

21.

Goodman

S. A.

Svyantek

D. J.

(1999). Person–organization fit and contextual performance: Do shared values matter. Journal of Vocational Behavior, 55(2), 254–275. https://doi.org/10.1006/jvbe.1998.1682

22.

Hartung

P. J.

(2005). Internationalizing career counseling: Emptying our cups and learning from each other. The Career Development Quarterly, 54(1), 12–16. https://doi.org/10.1002/j.2161-0045.2005.tb00136.x

23.

Heiligers

P. J. M.

(2012). Gender differences in medical students’ motives and career choice. BMC Medical Education, 12(1), 82–82. https://doi.org/10.1186/1472-6920-12-82

24.

Hofstede

(2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1), 1014. https://doi.org/10.9707/2307-0919.1014

25.

Hong

Romans

J. S. C.

Koch

J. M.

Ramakrishnan

(2022). Impact of cultural individualism and collectivism on protean and boundaryless career attitudes and Job satisfaction. Journal of Career Development, 49(1), 218–231. https://doi.org/10.1177/0894845320922608

26.

L.-t.

Bentler

P. M.

(1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424

27.

L. T.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

28.

Jackson

D. L.

Gillaspy

J. A.,

Jr Purc-Stephenson

(2009). Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychological Methods, 14(1), 6–23. https://doi.org/10.1037/a0014694

29.

Jin

Rounds

(2012). Stability and change in work values: A meta-analysis of longitudinal studies. Journal of Vocational Behavior, 80(2), 326–339. https://doi.org/10.1016/j.jvb.2011.10.007

30.

Joo

B. K. B.

Bennett

R. H.,

III (2018). The influence of proactivity on creative behavior, organizational commitment, and job performance: Evidence from a Korean multinational. Journal of International and Interdisciplinary Business Research, 5(1), 1–20. https://doi.org/10.58809/gwxr1454

31.

Joo

B. K. B.

Park

(2010). Career satisfaction, organizational commitment, and turnover intention: The effects of goal orientation, organizational learning culture and developmental feedback. Leadership and Organization Development Journal.

32.

Kanfer

Ackerman

P. L.

(2004). Aging, Adult Development, and Work Motivation. The Academy of Management Review, 29(3), 440–458. https://doi.org/10.2307/20159053

33.

Kordbacheh

Shultz

K. S.

Olson

D. A.

(2014). Engaging mid and late career employees: The relationship between age and employee engagement, intrinsic motivation, and meaningfulness. Journal of Organizational Psychology, 14(1), 11.

34.

Lee

J. Y.

Chen

C. L.

Kolokowsky

Hong

Siegel

J. T.

Donaldson

S. I.

(2021). Development and validation of the career crafting assessment (CCA). Journal of Career Assessment, 29(4), 717–736. https://doi.org/10.1177/10690727211002565

35.

Little

T. D.

(1997). Mean and covariance structures (MACS) analyses of cross-cultural data: Practical and theoretical issues. Multivariate Behavioral Research, 32(1), 53–76. https://10.1207/s15327906mbr3201_3

36.

Lyons

S. T.

Schweitzer

E. S. W.

(2015). How have careers changed? An investigation of changing career patterns across four generations. Journal of Managerial Psychology, 30(1), 8–21. https://doi.org/10.1108/JMP-07-2014-0210

37.

Lytle

M. C.

Foley

P. F.

Cotter

E. W.

(2015). Career and retirement theories: Relevance for older workers across cultures. Journal of Career Development, 42(3), 185–198. https://doi.org/10.1177/0894845314547638

38.

Macky

Gardner

Forsyth

(2008). Generational differences at work: Introduction and overview. Journal of Managerial Psychology, 23(8), 857–861. https://doi.org/10.1108/02683940810904358

39.

Meade

A. W.

Kroustalis

C. M.

(2006). Problems With Item Parceling for Confirmatory Factor Analytic Tests of Measurement Invariance. Organizational Research Methods, 9(3), 369–403. https://doi.org/10.1177/1094428105283384

40.

Muthén

L. K.

Muthén

B. O.

(1998-2012). Mplus user’s guide (Seventh edition). Muthén and Muthén.

41.

Nunnally

J. C.

(1978). Psychometric theory. McGraw-Hill.

42.

Oprea

Păduraru

Iliescu

(2022). Job crafting and intent to leave: The mediating role of meaningful work and engagement. Journal of Career Development, 49(1), 188–201. https://doi.org/10.1177/0894845320918666

43.

Oprea

B. T.

Ștefan

M. C.

(2019). Scala pentru modificarea postului: Adaptare la limba română. Psihologia Resurselor Umane, 13(1), 39–52.

44.

Orpen

(1984). The effect of job tenure on the relationship between perceived task attributes and job satisfaction. The Journal of Social Psychology, 124(1), 135–136. https://doi.org/10.1080/00224545.1984.9924549

45.

Satorra

Bentler

P. M.

(2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75(2), 243–248. https://10.1007/s11336-009-9135-y

46.

Schaufeli

W. B.

Bakker

A. B.

Salanova

(2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and Psychological Measurement, 66(4), 701–716. https://doi.org/10.1177/0013164405282471

47.

Schmitt

Kuljanin

(2008). Measurement invariance: Review of practice and implications. Human Resource Management Review, 18(4), 210–222. https://10.1016/j.hrmr.2008.03.003

48.

Schumacker

R. E.

Lomax

R. G.

(2010). A beginner’s guide to structural equation modeling (3rd ed.). Taylor and Francis Group.

49.

Seibert

S. E.

Crant

J. M.

Kraimer

M. L.

(1999). Proactive personality and career success. The Journal of Applied Psychology, 84(3), 416–427. https://doi.org/10.1037/0021-9010.84.3.416

50.

Shuck

Twyford

Reio

T. G.,

Jr. Shuck

(2014). Human resource development practices and employee engagement: Examining the connection with employee turnover intentions. Human Resource Development Quarterly, 25(2), 239–270. https://doi.org/10.1002/hrdq.21190

51.

Stahl

G. K.

Chua

C. H.

Caligiuri

Cerdin

J. L.

Taniguchi

(2009). Predictors of turnover intentions in learning-driven and demand-driven international assignments: The role of repatriation concerns, satisfaction with company support, and perceived career advancement opportunities. Human Resource Management Published in Cooperation with the School of Business Administration, 48(1), 89–109.

52.

Steger

M. F.

Dik

B. J.

Duffy

R. D.

(2012). Measuring meaningful work: The work and meaning inventory (WAMI). Journal of Career Assessment, 20(3), 322–337. https://doi.org/10.1177/1069072711436160

53.

Sturges

Guest

Conway

Davey

K. M.

(2002). A longitudinal study of the relationship between career management and organizational commitment among graduates in the first ten years at work. Journal of Organizational Behavior, 23(6), 731–748. https://doi.org/10.1002/job.164

54.

Sun

(2005). Assessing goodness of fit in confirmatory factor analysis. Measurement and Evaluation in Counseling and Development, 37(4), 240–256. https://doi.org/10.1080/07481756.2005.11909764

55.

Super

D. E.

(1980). A life-span, life-space approach to career development. In Brown

Brooks

(Eds.), Career choice and development: Applying contemporary theories to practice (pp. 197–261). Jossey-Bass.

56.

Tavakol

Dennick

(2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd

57.

Tims

Bakker

A. B.

Derks

(2012). Development and validation of the job crafting scale. Journal of Vocational Behavior, 80(1), 173–186. https://doi.org/10.1016/j.jvb.2011.05.009

58.

Urbach

Den Hartog

D. N.

Fay

Parker

S. K.

Strauss

(2021). Cultural variations in whether, why, how, and at what cost people are proactive: A followership perspective. Organizational Psychology Review, 11(1), 3–34. https://doi.org/10.1177/2041386620960526

59.

Van Leeuwen

E. H.

Taris

T. W.

Van den Heuvel

Knies

van Rensen

E. L. J.

Lammers

J. W. J.

(2021). A career crafting training program: Results of an intervention study. Frontiers in Psychology, 12, 664453. https://doi.org/10.3389/fpsyg.2021.664453

60.

Van Wingerden

Van der Stoep

(2018). The motivational potential of meaningful work: Relationships with strengths use, work engagement, and performance. PLoS ONE, 13(6), Article e0197599. https://doi.org/10.1371/journal.pone.0197599

61.

Vaske

J. J.

Beaman

Sponarski

C. C.

(2017). Rethinking internal consistency in Cronbach’s alpha. Leisure Sciences, 39(2), 163–173. https://doi.org/10.1080/01490400.2015.1127189

62.

Vîrgă

Zaborilă

Sulea

Maricuțoiu

(2020). Roumanian adaptation of Utrecht work engagement scale: The examination of validity and reliability. Psihologia Resurselor Umane, 7(1), 58–74. https://doi.org/10.24837/pru.v7i1.402

63.

Wrzesniewski

Dutton

J. E.

(2001). Crafting a job: Revisioning employees as active crafters of their work. Academy of Management Review, 26(2), 179–201. https://doi.org/10.5465/amr.2001.4378011