Validation of the subjective career success scale among Brazilian professorsABSTRACT
Purpose This article aims to present the validation of the Dual Aspect Importance & Achievement Career Success Scale (DAIA-CSS) in Brazilian Portuguese in the context of federal university professors.
Design/methodology/approach The authors used Confirmatory Factor Analysis (CFA) to examine the adequacy of the factorial structure of the seven dimensions of the measurement model. The validation was performed based on a sample of 999 university professors.
Findings Through the CFA, the adjustments to the measurement model according to the additions of modification indices and correlations between the items, the verification of the indicators of general adjustment of the model and convergent and the discriminant validity, evidence of validity was found for the factorial structure of the DAIA-CSS measurement instrument for its application in Brazilian Portuguese.
Originality/value This research presents, as a theoretical contribution, the validation of an instrument for measuring subjective career success in the context of Brazilian university professors. Understanding the dimensions of subjective success can help individuals better understand themselves and their careers, which can also contribute to other areas of their personal lives. Finally, understanding individual differences in the importance and achievement of subjective success can contribute to developing human resource policies and practices for organizations.
Subjective career success; Career success; University professors; Validation; Confirmatory factor analysis
RESUMO
Purpose O objetivo deste artigo é apresentar a validação da escala The Dual Aspect Importance & Achievement Career Success Scale (DAIA-CSS) em português do Brasil, no contexto de professores universitários federais.
Design/methodology/approach Os autores utilizaram a Análise Fatorial Confirmatória (AFC) para examinar a adequação da estrutura fatorial das sete dimensões do modelo de mensuração. A validação deu-se a partir de uma amostra de 999 professores universitários.
Findings Por meio da AFC, dos ajustes no modelo de mensuração de acordo com os acréscimos dos índices de modificação e correlações entre os itens, da verificação dos indicadores de ajuste geral do modelo e das validades convergente e discriminante, foram encontradas evidências de validade para a estrutura fatorial do instrumento de mensuração DAIA-CSS para sua aplicação em língua portuguesa.
Originality/value Esta pesquisa apresenta, como contribuição teórica, a validação de um instrumento para mensuração do sucesso subjetivo na carreira no contexto de professores universitários brasileiros. Para os indivíduos, conhecer as dimensões do sucesso subjetivo pode auxiliar na compreensão de si mesmos e de suas carreiras, o que pode contribuir para outras áreas da vida pessoal. Por fim, para as organizações, a compreensão das diferenças individuais quanto à importância e realização do sucesso subjetivo pode contribuir para o desenvolvimento de políticas e práticas de recursos humanos.
Sucesso subjetivo na carreira; Sucesso na carreira; Professores universitários; Validação; Análise fatorial confirmatória
1 INTRODUCTION
Careers can be described in two fundamentally distinct ways: objective and subjective (Arthur et al., 2005). Hughes first introduced this differentiation in 1937. While the objective career concerns the organizational or social interpretations of an individual’s professional situation, the subjective career encompasses the individual’s appreciation and interpretation of their life as a whole, including the career itself (Hughes, 1937; Stebbins, 1970; Barley, 1989; Arthur, 1994).
Success results from an individual’s career experiences (Arthur et al., 2005) and can be determined by objective and subjective measures (Judge et al., 1995; Arthur et al., 2005). While objective success (OS) is assessed by visible criteria—such as wage, promotions, and hierarchical position in the organization—subjective success (SS) concerns how the individual personally considers and evaluates their career (Van Maanen, 1977; Jaskolka & Beyer, 1985; Greenhaus et al., 1990; Judge et al., 1995; Judge et al., 1999; Bagdadli & Gianecchini, 2019; Giraud et al., 2019).
Gunz and Heslin (2005) highlighted that, although much of the research on career success is operationalized through objective measures, an alternative perspective should be analyzed in more detail. It is necessary to consider that the individuals themselves can decide what success means to them, that is, from a subjective perspective.
The most recent SS measurement scale—the Dual Aspect Importance & Achievement Career Success Scale (DAIA-CSS) (Briscoe et al., 2021)—goes beyond understanding how satisfied and fulfilled the individual is with the level of success they have achieved in their career; it also measures how important each aspect of the career is to the respondent. This represents an advancement over previously proposed scales, which only measure how happy (or unhappy) someone is with their career success. However, they do not adequately assess whether certain dimensions of success truly matter to the individual. The scale developed by Jon P. Briscoe and colleagues, although published in 2021, had already been under construction for several years by the research team from the 5C group—Cross-Cultural Collaboration on Contemporary Careers (https://5c.careers/)—and had been referenced in previous works.
This article aims to present the validation of the DAIA-CSS scale in Brazilian Portuguese in the context of federal university professors. According to Shockley et al. (2016) and Akkermans and Kubasch (2017), career success is a topic of considerable research interest. Seibert et al. (2024) highlight that the consequences of the COVID-19 pandemic are still being experienced, and, therefore, this is a promising time to address career success. Studies on the subject primarily focus on defining successful careers, predictors, and taxonomies, including SO and SS (Shockley et al., 2016; Akkermans & Kubasch, 2017). Despite that, little is known about teachers’ opinions regarding success and satisfaction in their careers (Araújo, Miranda, & Pereira, 2017; Gubler et al., 2019). Generally, subjective success research remains scarce (Kundi et al., 2023). It is also noteworthy that studying career success in the context of specific professional groups can significantly contribute to the understanding of the subject (Akkermans & Kubasch, 2017), as definitions of career success depend on the context of individuals (Fernández et al., 2023).
2 CAREER SUCCESS
Research on career success is grounded in contemporary career concepts (Arthur et al., 2005). If a career encompasses an individual’s personal work experiences (Arthur et al., 1989), career success is the result of such experiences (Arthur et al., 2005) and their expectations of success (Fernández et al., 2023). Career success can be understood as “[…] the positive psychological or work-related outcomes or achievements one has accumulated as a result of one’s work experiences” (Judge et al., 1995, p. 2). Similarly, Arthur et al. (2005, p. 179) define career success as “[…] the accomplishment of desirable work-related outcomes at any point in a person’s work experiences over time”.
Like a career, career success can be objective or subjective (Van Maanen, 1977; Seibert & Kraimer, 2001; Ng et al., 2005). Both concepts are interdependent (Arthur et al., 2005) and essential to understanding success (Judge et al., 1995; Abele et al., 2011), although they are conceptually and empirically distinct (Ng et al., 2005; Suutari et al., 2017).
Objective success (OS) can be judged by third parties and is determined by relatively objective and visible criteria (Jaskolka & Beyer, 1985). This type of success “[…] may be defined as an external perspective that delineates more or less tangible indicators of an individual’s career situation” (Arthur et al., 2005, p, p. 179).
Also called extrinsic success, OS refers to factors that can be easily perceived in an individual’s career, such as their remuneration (Van Maanen, 1977; Judge et al., 1995; Lyness & Thompson, 2000; Boudreau et al., 2001; Seibert & Kraimer, 2001; Ng et al., 2005; Rode, et al., 2008; Bagdadli & Gianecchini, 2019; Giraud et al., 2019), position in the hierarchical structure of the organization, or occupational status (Van Maanen, 1977; Lyness & Thompson, 2000; Boudreau et al., 2001; Seibert & Kraimer, 2001; Chughtai, 2018; Bagdadli & Gianecchini, 2019), ascendancy or number of promotions, i.e., how many times an individual has progressed in the organization during a given period (Judge et al., 1995; Boudreau et al., 2001; Ng et al., 2005; Rode et al., 2008; Abele, Spurk, & Volmer, 2011; Bagdadli & Gianecchini, 2019), and labor market participation (Boudreau et al., 2001; Giraud et al., 2019).
Subjective success (SS), or intrinsic career success, refers to how the individual considers and qualifies their career based on what they judge as valuable (Van Maanen, 1977); namely, it is an individual understanding and judgment (Judge et al., 1995; Arthur et al., 2005). Abele et al. (2011, p. 197) establish that when measuring SS, “[…] an individual compares his/her career relative to personal standards and aspirations”. SS is “[…] much less likely to be influenced by comparisons relative to peers” (Seibert et al., 2024).
The career satisfaction measurement scale proposed by Greenhaus et al. (1990) is usually considered the best unidimensional measure of career success (Briscoe et al., 2021) and has been used in several studies (e.g., Judge et al., 1995; Boudreau et al., 2001; Hofmans et al., 2008; Abele et al., 2011; Visentin, 2015; Oliveira, 2017; Teixeira & Costa, 2017; Chughtai, 2018; Suutari et al., 2017; Janssen et al., 2021; Bazine et al., 2024). Unidimensional measures average individuals’ responses across multiple items/factors and, as a result, provide an overall assessment of subjective career success (Briscoe et al., 2021). Such measures also exhibit high alpha values for the correlations between the items included in the scale (Arthur et al., 2005).
Mayrhofer et al. (2016) identified that, for a long time, researchers measured career success through indicators such as remuneration, promotions, and job satisfaction. Despite the importance of these measures, they “[…] do not capture the range and nuance of what people consider when they think about their career success” (Mayrhofer et al., 2016, p. 197). In the same vein, despite the wide use of the scale proposed by Greenhaus et al. (1990), Arthur et al. (2005) state that several psychosocial approaches have suggested that subjective career success is composed of multiple dimensions. Similarly, qualitative research by Fernández et al. (2023) also provided evidence that career success is a multidimensional concept. For Arthur et al. (2005, p. 194), “subjective careers and subjective career success seem too important to be prematurely constrained to any one-dimensional interpretation.” Shockley et al. (2016) mention other dimensions that should be considered when measuring SS in addition to career satisfaction. Akkermans et al. (2020) also indicate that SS is a multidimensional construct that goes beyond career satisfaction.
Unlike unidimensional measures, multidimensional scales allow us to understand how much each factor or dimension of career success contributes to the overall subjective success of an individual’s career (Briscoe et al., 2021). As a multidimensional construct, SS can be observed more recently in the scale proposed by Briscoe et al. (2021).
3 DAIA-CSS
The Dual Aspect Importance & Achievement Career Success Scale (Briscoe et al., 2021) was constructed from studies done in four phases. The objective of the first phase was to create a cross-cultural list of SS meanings, including different countries and cultures. In-depth qualitative research was conducted with 226 individuals from 11 countries, including Brazil, based on the following question: “Looking back at your experience and your career thus far: what does ‘career success’ mean to you?” (Briscoe et al., 2021, p, p. 16). From this question, a list of 63 meanings of career success was developed.
In phase 2, using the list obtained in the previous phase, 364 individuals from 13 countries were asked to organize the meanings of success into groups. The goal was to create provisional groups of meanings of career success (Briscoe et al., 2021). From this, 11 items were excluded because they did not belong to any group or appeared in several groups, leaving 52 meanings divided into 14 groups of subjective career success.
In the third phase, the objective was to reduce the number of items and dimensions and determine the discriminant and convergent validity of the scale. At this point, it is worth noting that the DAIA-CSS is the first SS scale with a dual response format: it measures both how important a given item is to the respondent and how fulfilled/satisfied they are with that SS item in their career (Briscoe et al., 2021). A pilot questionnaire was constructed and applied to 4,438 workers—with at least two years of experience—from 16 countries. The Confirmatory Factor Analysis technique was conducted separately for both aspects of the career (importance and achievement). At the end of this process—which involved excluding items with low factor loadings, removing factors with too few items, and merging factors with high correlation—the factor structure was composed of seven factors.
The final structure of the scale is composed of the following dimensions: learning and development (4 items), work-life balance (3 items), positive impact (3 items), entrepreneurship (3 items), positive work relationships (4 items), financial security (3 items), and financial success (3 items) (Briscoe et al., 2021). The DAIA-CSS consists of seven dimensions that are globally relevant, meaning they are regularly considered by individuals around the world when evaluating their careers (Mayrhofer et al., 2016; Briscoe et al., 2021). Table 1 presents the definitions of each of the scale’s dimensions.
The fourth and final phase aimed to validate the factor structure from phase 3 in a new sample, as well as to examine the invariance of the scale across cultures and test differences between countries. A total of 13,859 individuals from 20 countries participated in this phase, with the sample from each country including at least 400 participants. The results demonstrated that the scale can capture differences in career SS between countries (Briscoe et al., 2021).
The DAIA-CSS is a scale used to measure subjective career success based on two main contributions: (1) “[…] is not only a measurement instrument that addresses remaining issues in the measurement of subjective career success; it should also be seen as a vehicle for identifying and addressing new theoretical questions about subjective career success” (Briscoe et al., 2021, p. 6); and (2) “[…] it measures career success in terms of the value (what we refer to as importance) that people place on different aspects of their careers, as well as their degree of satisfaction (or achievement) with the level of success they have reached” (Briscoe et al., 2018, p. 126). Thus, it is possible to measure the difference between the perceived importance (i-SS) and the achievement (a-SS) of subjective career success (Briscoe et al., 2018).
4 METHODOLOGICAL PROCEDURES
To validate the DAIA-CSS in Brazilian Portuguese in the context of federal university professors, a pre-test was first conducted with 10 professors. Chagas (2000) discusses the relevance of the pre-test to minimize or, preferably, eliminate any potential issues or doubts before the questionnaire is applied. The original and translated scale (with the adaptations suggested highlighted in the pre-test) is available in Appendix A.
Confirmatory Factor Analysis (CFA) was used to examine the adequacy of the factor structure of the seven dimensions of the DAIA-CSS measurement model (MM): (1) Learning and Development (LD), (2) Work-Life Balance (WLB), (3) Positive Impact (PI), (4) Entrepreneurship (ENT), (5) Positive Work Relationships (PWR), (6) Financial Security (FES), and (7) Financial Success (FSUC).
The population of this study includes in-service teachers working at federal universities at different levels of the higher education teaching career, as well as visiting, substitute, temporary, and guest professors. According to the National Institute for Educational Studies and Research Anísio Teixeira (2019), the population of in-service teachers at federal universities is estimated at 93,672.
The sample, composed of professors working at Brazilian federal universities, was a non-probabilistic, convenience sample (Hair Jr. et al., 2005), based on the possibility of finding the professor’s contact email on the university website and the professor’s availability to respond to the survey. Another criterion for inclusion in the sample was that the professor should teach at least undergraduate courses. The analysis used—CFA—requires a minimum sample size. To calculate the sample size for applying this technique, “[…] as a general rule, the minimum is to have at least five times more observations than the number of variables to be analyzed, and the most acceptable size would be a ratio of ten to one” (Hair Jr. et al., 2009, p. 108). Since 23 items (referring to DAIA-CSS) would be subjected to this technique, at least 230 responses were necessary. However, the sample size was more than four times the required size, totaling 999 respondents.
The analysis was performed for the importance (i-SS) and achievement (a-SS) models of subjective success in four stages: (i) adjustments based on the modification indices (MI), (ii) correlations between the items; (iii) verification of the overall fit indices of the model; (iv) verification of convergent and discriminant validity.
The first two steps consisted of estimating different measurement models (MMs) with the addition of new parameters. The first MM estimated was the most basic, including only the factor loadings (λ). From the second step onwards, correlations (ρ) were added between the items, as suggested by the MIs. This iterative process generated eight models for importance (i-SS) and 12 models for achievement (a-SS). The stopping criterion used was the estimation of models that did not have MIs greater than 10 (Cole et al., 2005).
The third stage concerns the analysis of the measurement model’s general fit indices, which include the chi-square test (χ2), degrees of freedom (df), and their respective adjustment variations (Δχ2, Δχ2/df, statistical significance of differences); the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Adjusted Goodness of Fit Index (AGFI), and Goodness of Fit Index (GFI), which ideally should be > 0.95; the Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA), which should ideally be < 0.05 for models with better fit. Additionally, the lower (RMSEA-LI) and upper (RMSEA-LS) limits for RMSEA were also reported (Hair Jr. et al., 2018).
The fourth stage assessed both convergent and discriminant validities. “Convergent validity assesses the degree to which two measures of the same concept are correlated” (Hair Jr. et al., 2009, p. 126). Thus, the indicators of a latent variable (LV) must share a high proportion of variance (Hair Jr. et al., 2009). To assess convergent validity, the factor loadings (λ) were initially analyzed for their statistical significance (p-value or p). At the item level, convergent validity is considered valid when the following null hypothesis (H0) is rejected: the factor loading (λ) is equal to zero. Therefore, evidence for H1— that the factor loading (λ) is different from zero— can be established.
To assess convergent validity at the latent variable (LV) level, three indicators were used: the alpha (α) (Cronbach, 1951) and omega (Ω) (McDonald, 1999) coefficients, which must be > 0.7, and the Average Variance Extracted (AVE) (Bacon, Sauer, & Young, 1995), which must be > 0.5.
Discriminant validity indicates “the degree to which two similar concepts are distinct” (Hair Jr. et al., 2009, p. 126). Two criteria were used for assessing discriminant validity: the Fornell-Larcker criterion (Fornell & Larcker, 1981) and the heterotrait-monotrait ratio (HTMT) (Henseler, Ringle, & Sarstedt, 2015). For the first criterion, the square root of the Average Variance Extracted (AVE) must be greater than the correlations between pairs of correlations (ρ) (Fornell & Larcker, 1981). For the second criterion, the HTMT coefficients must be less than 1. More specifically, two cutoff points were used: a more flexible one, HTMT < 0.9 (Henseler et al., 2015), and a more stringent one, HTMT < 0.85 (Voorhees et al., 2016).
The methodological procedures have been presented, and the results and discussions follow.
5 RESULTS AND DISCUSSIONS
To validate the DAIA-CSS in Brazilian Portuguese in the context of federal professors, adjustments were initially made based on the additions suggested by the modification indices (MI) and correlations between the items. As shown in Table 2, the Base model (“00”) already exhibits good general fit indices for the measurement models (MMs) of importance (i-SS) and achievement (a-SS) according to the AGFI and GFI (since both are > 0.95), as well as for the SRMR (both < 0.05). It is also important to highlight that, in the “00” model, for the MM of i-SS, the RMSEA confidence intervals indicate a good fit (RMSEA, RMSEA-LI, and RMSEA-LS < 0.05). However, for the MM of a-SS, the base model only shows a good fit at the lower limit of RMSEA (RMSEA-LI < 0.05), with RMSEA and RMSEA-LS > 0.05.
Despite these differences between i-SS and a-SS, the first indication of MI was the same for both MMs: a correlation between LD3a (Continuously learning throughout my career) and LD4a (Doing work that gives me the opportunity to learn)—both items directly addressing the topic of learning. This iterative process of adjusting the MIs was carried out based on the theoretical and semantic meaning of the items of the DAIA-CSS scale up to the limit of MI > 10 (Cole et al., 2005).
In Table 2, it is possible to note that all modifications made are statistically significant (p-value < 0.01). The final MM of i-SS was reached after eight adjustments, and the final MM of a-SS was reached after 12 adjustments. Finally, the two MMs—importance and achievement—presented satisfactory levels of overall fit for all indicators used.
Table 2 presents the factor loadings (λ) and r2 obtained in this research for each item of the DAIA-CSS, both for the importance aspect (i-SS) and for achievement (a-SS). The factor loadings from Phase 4 of the research by Briscoe et al. (2021) and colleagues are also shown. This phase was conducted with 13,859 individuals from 20 countries. The presentation of these data in the same table aims to provide a better understanding of the differences and similarities between this study and the original DAIA-CSS validation study.
The λ indicates the correlation between the dimensions and the items, being fundamental for understanding a specific factor (Hair Jr. et al., 2009). In this case, the dimensions are also called 1st-order latent variables (LV)—LD, ENT, WLB, PI, PWR, FES, FSUC) —, and the items are each of the assertions of the scale—LD1, LD2, …, FSUC3). Larger factor loadings make the item more representative of the dimension (Hair Jr. et al., 2009), that is, the higher the λ, the more the 1st-order LV can explain the item.
The squared factor loadings (r2) indicate the percentage of variance that a LV can explain for the item (Hair Jr. et al., 2009). For the item to share 50% of its variability with the dimension, the factor loading must be greater than 0.70. Factor loadings between 0.3 and 0.4 meet the minimum level for interpreting the factor structure, and factor loadings of 0.5 or higher are considered significant.
According to the results in Table 3, in general, in this research, the λ of the achievement SS model are higher than those of the importance model, except for the items ENT2 (Owning my own company), ENT3 (Running my own business), PWR2 (Experiencing positive relationships with superiors), FSUC2 (Receiving incentives, perks, or bonuses), and FSUC3 (Steadily making more money). It is also noted that, except for some items, the factor loadings obtained in Briscoe et al.’s research (2021) are higher than those obtained in this study, both in terms of importance and achievement. Despite this, it is important to highlight that all factor loadings obtained in this research were statistically significant, meaning they are different from 0 (p-value < 0.01), and are very close to 0.4 (only AD3 in i-SS, with λ = 0.393) or higher, and therefore meet the recommendations in the literature.
The convergent and discriminant validities for the i-SS and a-SS MMs were also assessed. As shown in Table 4, the achievement MM (a-SS) generally has higher levels of convergent validity than the importance MM (i-SS).
In the MM of a-SS, except for the Financial Success dimension (FSUC), all other first-order LVs exhibited convergent validity coefficients above or very close to those recommended by the literature: α and Ω > 0.7 and AVE > 0.5. In the Positive Impact dimension (PI), for example, Ω = 0.691 and AVE = 0.457. For the Learning and Development dimension (LD), AVE = 0.425. In the Financial Success dimension (FSUC), despite the low Ω and AVE indices (0.468 and 0.295, respectively), the value of α = 0.686 is close to the ideal. In Table 4, the cells highlighted in yellow indicate a lack of convergent validity, including the coefficients that were close to those recommended by the literature.
In the i-SS MM, only the entrepreneurship LV (ENT) reached coefficients above the cutoff point mentioned in the literature for all convergent validity indicators used. The Positive Work Relationships dimension (PWR), despite having α and Ω > 0.7, had AVE < 0.5, although it was close to the ideal. The Work-Life Balance dimension (WLB) presented only Ω > 0.7, with α and AVE being only close to the ideal. The other i-SS dimensions exhibited lower levels of convergent validity.
In the scale validation by Briscoe et al. (2021), the Ω values for the SS dimensions are presented, and it can be seen that, except for the Positive Impact dimension (PI) in the achievement model (a-SS)—in which Ω = 0.699—all the others presented Ω > 0.7. It is also noticeable that, generally, the authors found better Ω indices in the achievement model than in the importance model, a result similar to that identified in this research.
The two main points of lack of discriminant validity occur between (1) Learning and Development (LD) and Positive Impact (PI) and (2) Financial Success (FSUC) and Financial Security (FES). In Table 4, the cells highlighted in gray indicate a lack of discriminant validity.
According to the Fornell and Larcker (FL) criterion, the square root of the AVE must be greater than the correlation between pairs (ρ). It can be seen from the results presented in Table 4 (highlighted in gray) that there is no discriminant validity between LD and PI for the two MMs, in other words, for both i-SS and a-SS. This result differs from that presented by the heterotrait-monotrait ratio criterion (HTMT), which indicates discrimination at the least rigorous level (HTMT < 0.9). Still, it does not indicate discrimination at the more rigorous level (HTMT < 0.85) in the a-SS MM, since HTMT = 0.858.
For the i-SS MM, according to the FL criterion, no discrimination was observed between LD and PWR, as √AVE = 0.472 is smaller than the correlation of 0.512. According to the HTMT criterion, however, there is discriminant validity between these two variables when considering both cutoff points: the more flexible (HTMT < 0.9) and the more rigorous (HTMT < 0.85), since HTMT = 0.474.
For the a-SS MM, using the FL criterion, a lack of discrimination between Financial Success (FSUC) and the other dimensions was still observed, except for Positive Impact (PI) — in this case, √AVE = 0.543 is only greater than the correlation between FSUC and PI, which is 0.491. Despite this, there is discriminant validity between FSUC and all other dimensions when considering the HTMT criterion, both at the most flexible and the most rigorous cutoff points.
Based on the results presented, it can be stated that there is evidence of validity for the factorial structure of the DAIA-CSS measurement instrument.
6 FINAL CONSIDERATIONS
The objective of this article was to present the validation of the Dual Aspect Importance & Achievement Career Success Scale (DAIA-CSS) in Brazilian Portuguese in the context of federal university professors. Evidence of validity was found for the factorial structure of the DAIA-CSS measurement instrument through Confirmatory Factor Analysis (CFA), adjustments to the measurement model based on modification indexes and correlations between items, and verification of the general model fit indicators, as well as convergent and discriminant validities.
A limitation of this study is that the variables used by Briscoe et al. (2021) as antecedents of SS were not included, namely, employability, work engagement, organizational commitment, and turnover intention. Therefore, the relationships estimated in this paper may be underestimated or overestimated, depending on their correlation with the variables that explain SS.
Another limitation concerns that, although the DAIA-CSS items were presented randomly, the same order of questions was shown to all professors. This introduces a second limitation related to the halo effect (Nisbett & Wilson, 1977). Finally, it is worth noting that the results presented may not represent the entire population of federal professors in Brazil. Although the sample included a significant number of respondents, it was non-probabilistic and based on convenience, which limits the generalizability of the results to all federal professors and their unique characteristics.
Despite the limitations presented, the validation of the Dual Aspect Importance & Achievement Career Success Scale (DAIA-CSS) in Brazilian Portuguese, within the context of federal university professors, represents a theoretical contribution of this article. In practical terms, understanding the factors contributing to subjective career success can help individuals gain self-awareness, leading to greater satisfaction with life, improved mental health, and psychological well-being (Shockley et al., 2016; Choi & Nae, 2020). Specifically, for those in teaching careers, in which there is significant interaction with others—whether with students, colleagues, superiors, or peers—self-knowledge can be crucial for achieving success in one’s career. For organizations, understanding individual differences in the importance and achievement of subjective success can inform the development of human resource policies and practices. A positive organizational environment can enhance subjective success and reduce turnover intentions (Lehtonen, Nokelainen, Rintala, & Puhakka, 2022).
Future works could explore the relationship between subjective career success (SS) and other career-related aspects, such as career decision-making, health and well-being, employability, work-family balance, and/or work-family conflict. Additionally, it may be valuable to investigate the potential need to adapt the DAIA-CSS to different contexts.
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APPENDIX A – ORIGINAL AND TRANSLATED DAIA-CSS
Please read the list of Career Aspects in the left column. Then, for each Career Aspect, answer: How important do you consider this aspect to how you view career success, and whether you are happy with the level you have achieved in this career aspect. / Por favor, leia a lista dos Aspectos de Carreira na coluna à esquerda. Então, para cada Aspecto de Carreira, responda: Quão importante você considera esse aspecto para o modo como você vê sucesso na carreira; e se você está feliz com o nível que alcançou nesse aspecto de carreira.
Aspect of Importance: Thinking about success in my career, I consider this career aspect... Not at all important (1); Not important (2); Indifferent (3); Somewhat important (4); Very important (5). / Aspecto de Importância: Pensando sobre sucesso na minha carreira, eu considero este aspecto de carreira... Nada importante (1); Não importante (2); Indiferente (3); Um pouco importante (4); Muito importante (5).
Achievement Aspect: Regarding this career aspect, I have reached a level that I am happy with... Strongly disagree (1); Disagree (2); Neither agree nor disagree (3); Agree (4); Strongly agree (5). / Aspecto de Realização: Em relação a este aspecto de carreira, eu alcancei um nível com o qual eu estou feliz... Discordo totalmente (1); Discordo (2); Nem concordo nem discordo (3); Concordo (4); Concordo totalmente (5).
Edited by
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Edited by
Jordana Marques Kneipp
