Expanding career adaptability: Connections as a critical component of career success

Gaile, Anita; Baumane Vitolina, Ilona; Stibe, Agnis; Kivipõld, Kurmet Article Expanding career adaptability: Connections as a critical component of career success European Journal of Management and Business Economics (EJM&BE) Provided in Cooperation with: European Academy of Management and Business Economics (AEDEM), Vigo (Pontevedra) Suggested Citation: Gaile, Anita; Baumane Vitolina, Ilona; Stibe, Agnis; Kivipõld, Kurmet (2024) : Expanding career adaptability: Connections as a critical component of career success, European Journal of Management and Business Economics (EJM&BE), ISSN 2444-8451, Emerald, Leeds, Vol. 33, Iss. 4, pp. 411-428, https://doi.org/10.1108/EJMBE-06-2023-0185 This Version is available at: https://hdl.handle.net/10419/325577 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Expanding career adaptability: connections as a critical component of career success Anita Gaile Riga Business School, Riga Technical University, Riga, Latvia Ilona Baumane Vitolina Faculty of Economics and Management, University of Latvia, Riga, Latvia Agnis Stibe The Business School, RMIT University, Ho Chi Minh City, Vietnam and Department of Informatics, EBIT, University of Pretoria, Pretoria, South Africa, and Kurmet Kivip~ old University of Tartu, Tartu, Estonia Abstract Purpose –Subjective career success has been widely researched by academics and researchers as it provides job and career satisfaction that can lead to the perceived life satisfaction of employees, as well as their engagement in organizations. This study demonstrates that subjective career success depends not merely on career adaptability but also on the connections people build throughout their professional lives. Design/methodology/approach –The study was conducted in the socioeconomic context of Latvia with a sample size of 390 respondents. Interpersonal behavioral factors from the perception of career success measure and the influence of the Career Adapt-Abilities Scale (CAAS) on subjective career success (two statements from Gaile et al., 2020) were used. The constructed research model was tested using the SPSS 28 and WarpPLS 8.0 software tools. The primary data analysis method used was partial least squares structural equation modeling (PLS-SEM). Then 12 moderators and their effects on the main relationships of the model were reviewed. Findings –The study confirms that relationships at work have the most significant effect on subjective career success, followed by control behaviors and curiosity behaviors. Moreover, a list of significant and insightful moderation effects was found, most significantly the relationship between connections and subjective career success. Originality/value –Until now, the CAAS was not integrated with the behaviors and attitudes that depict the social relationships of individuals at work. This study aims to narrow this gap by exploring whether (and, if so, how) career adaptability and interpersonal relationships in the workplace (i.e. professional connections) contribute to subjective career success. Keywords Career adaptability, Career success, Social connections, Career construction theory Paper type Research paper Introduction Career success and the role of proactive behaviors in shaping job-related development have been central topics in vocational behavior research (Spurk, 2021). The key prerequisites for maintaining a successful career include, among other factors, one’s strengths and core interests Expanding career adaptability 411 © Anita Gaile, Ilona Baumane Vitolina, Agnis Stibe and Kurmet Kivip~ old. Published in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode This paper forms part of a special section “Innovation, Knowledge, and Digitalization: Building Trust to Face Today’s Challenges”, guest edited by Alba Yela Ar anega and Juan Miguel Alc antara-Pilar. The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2444-8494.htm Received 12 June 2023 Revised 31 July 2023 29 September 2023 17 October 2023 15 November 2023 Accepted 17 November 2023 European Journal of Management and Business Economics Vol. 33 No. 4, 2024 pp. 411-428 Emerald Publishing Limited e-ISSN: 2444-8494 p-ISSN: 2444-8451 DOI 10.1108/EJMBE-06-2023-0185 (Brown and Lent, 2016), career competencies (Presti et al.,2022;Ayoobzadeh, 2022;FrancisSmythe et al.,2012;Kong, 2010;Kuijpers and Scheerens, 2006), one’s ability to overcome career hurdles (Ng and Feldman, 2014), the application of a boundaryless career approach (Bartel, 2021;Arthur and Rousseau, 1996), opportunities to follow one’s professional calling (Bloom et al.,2021;Praskova et al.,2014) and career adaptability (Savickas and Porfeli, 2012). The widely used Career Adapt-Abilities Scale (CAAS), grounded in career construction theory by Savickas and Porfeli (2012), portrays individuals as active creators of their work environments. This theory highlights the pivotal role of everyday behaviors and choices in crafting careers. The CAAS centers around four key behaviors, known as the 4 C’s–curiosity, concern, confidence and control –that are established antecedents of career success, supported by substantial empirical evidence (Rudolph et al., 2017). This study bridges a gap by proposing the inclusion of workplace social relationships, labeled as “Connections,”as the fifth C in the CAAS. In contrast to prior research, it highlights the overlooked role of moderators in individuals’career construction efforts. Categorizing these moderators into family life, job attributes, company size and demographic characteristics enhances our understanding of the diverse factors influencing career success, complementing the recognized importance of social capital in previous studies (Lin, 1999;Seibert et al., 2001;Chow, 2002;Lo Presti et al., 2019;Sou et al., 2022;Kauffeld and Spurk, 2022;Boat et al., 2022). The goal of this study is to examine the influence of the CAAS and interpersonal relationships on subjective career success. The study aims to develop an expanded Subjective Career Success Model by integrating interpersonal behavior with career adaptabilities. Data were collected from 390 individuals through social media platforms in Latvia and subjected to rigorous reliability and validity testing using SPSS 28 and WarpPLS 8.0 (specialized software for structural equation modeling). The findings of the study demonstrate the effectiveness of the newly developed model. The study concludes that interpersonal relationships significantly impact subjective career success, with sociodemographic factors like age, education and gender acting as moderators. Suggesting an expansion of the CAAS to include a fifth factor, “Connections,”the study advocates its use in future career success studies. These nuanced findings serve as a roadmap for individuals and career counselors, emphasizing the suitability of different behaviors in diverse life-cycle situations, considering various sociodemographic and family background factors. This paper is structured as follows. The literature review depicts the theories of vocational behavior, in particular Savickas and Profeli’s theory of career adaptability. Next, the role of interpersonal relationships and connections in career success is explored. The methodology addresses the measurement explanations and research model descriptions of this study. Following this are a discussion and the authors’conclusions. Literature review Career Adapt-Ability Scale within theories of vocational behavior According to Brown and Lent (2016), past vocational behavior research can be classified into three broad areas: (1) agency in career development, (2) equity in the workforce and (3) wellbeing in work and educational settings. Within the agency category, there is an emphasis on career success and career adaptability. To date, much attention has been paid to human capital within the following topics: career success (Ng et al., 2005;J€ arlstr€ om et al., 2020), psychological and demographic factors (Lyons et al., 2015;Haenggli and Hirschi, 2020; Zacher, 2014), social capital (J€ arlstr€ om et al., 2020;Barthauer et al., 2016), organizational support, organization–person fit (Ng and Feldman, 2014), leader–member exchange (e.g. Restubog et al., 2011;Spurk et al., 2014), member–member exchange (Drabe et al., 2015), EJMBE 33,4 412 performance (Dries et al., 2008) and career adaptability behaviors (Haenggli and Hirschi, 2020; Zacher, 2014) as factors that interplay in the development of a successful career. Savickas and Porfeli’s (2012) CAAS, a recognized antecedent for career success (Rudolph et al., 2017;Zacher, 2014), aligns with the focus on continuous individual activity in career development and success studies. Lee et al. (2021) note a lack of widespread criticism for the current CAAS version, establishing its positive impact on individuals and organizations. However, this paper critically examines the CAAS, highlighting its taskfocused nature and proposing the inclusion of a fifth “C”: connections, acknowledging the undervalued importance of social skills and interpersonal relationships (Rigby and Sanchis, 2006). Interpersonal relationships (connections) also play a significant role when individuals report their own subjective career success (e.g. Gattiker and Larwood, 1986;Shockley et al., 2016;Ng and Feldman, 2014;Poona et al., 2015). Thus, connections should be added as a separate element to the CAAS to build a more profound and coherent picture of the main factors that lead to career success (which are directly affected by each individual’s behavior). Social capital and interpersonal connections as predictors of career success The workplace’s social context is pivotal for success, especially in customer-centric industries like hospitality, retail, tourism and sales. Relationships stand as one of the three pillars of career success in the pursuit of a boundaryless career (Arthur and Defillippi, 2001), intertwined with motivation (Defillippi and Arthur, 1994). Interpersonal connections form the nucleus of career success predictors, encompassing trust (Wang, 2014), leader–member exchanges (Restubog et al., 2011;Spurk et al., 2014), member–member exchanges (Drabe et al., 2015), developmental networks (Chollet et al., 2021; Cheung et al., 2016;Cotton et al., 2011;Dobrow et al., 2012), mentoring (Ng and Feldman, 2014; Higgins and Kram, 2001;Lancau and Scandura, 2002;Kong, 2010;Defillippi and Arthur, 1994) and sponsored mobility (Maurer and Chapman, 2013). Numerous studies underscore how social capital impacts executive compensation (Belliveau et al., 1996;Burt, 1997), reduces turnover rates (Krackhardt and Hanson, 1993) and influences career orientations, especially for women (Rodrigues et al., 2019;Choi, 2019). Recent research extends this impact to the career and development outcomes of HR professionals (Gubbins and Garavan, 2016). Subjective career success Career success, encompassing job aspects, finances, interpersonal relationships, personal lives, learning and development, is influenced by an individual’s raised life standard and educational level, leading to varied needs (Harrington and Hall, 2007). This shift in understanding subjective career success emphasizes overall job and career satisfaction. Vocational behavior plays a crucial role in achieving the desired outcome of subjective career success. Possible moderators of subjective career success Recognizing the complexity of career behavior, the authors employ moderators to assess intervention effects across diverse research sample groups (MacKinnon, 2011). Previous studies highlight demographic factors, such as age (Van der Heijden et al., 2022), gender (Fernandez et al., 2023) and marital status (Agrawal and Singh, 2022), influencing subjective career success. To address this, employers are adopting work–family enrichment options (Awan et al., 2021) and age-adjusted human resource development policies (Van der Heijden et al., 2022). Expanding career adaptability 413 Job satisfaction has always been related to subjective career success, but it remains debated whether job satisfaction leads to perceived career success (Drabe et al., 2015; Schwormal et al., 2017) or subjective career success leads to job satisfaction (Lehtonen et al., 2022;Sou et al., 2022). Education is the foundation for the individual’s profession and perceived self-worth in the labor market (Duta et al., 2021;Hildenbrand, 2015;S€ onmez et al., 2021). Education makes job crafting possible, which lately has been introduced as an antecedent of subjective career success (Kundi et al., 2022;Lo Presti et al., 2023). Microenvironments and macroenvironments impact individuals’career satisfaction. Family influence, while potentially contributing to career success, may also hinder it if careerbuilding overshadows family commitments (Liu and Yu, 2021). Organizations, as a microenvironment, offer career resources like networking and mentoring, fostering career success through enhanced learning and professional growth opportunities (Agrawal and Singh, 2022;Lehtonen et al., 2022). Larger organizations, with increased resources, are positioned to provide better support for successful careers. Considering the macroenvironment is crucial in evaluating career success. Socioeconomic status, global income inequality and dependence on institutional capital like cities are pivotal factors (Awan et al., 2021;Bagdadli et al., 2021;Fernandez et al., 2023;Guo and Baruch, 2021). Unemployment experience, tied to macroeconomic shifts, significantly impacts individuals’ employability and career success (Manzoni and Mooi-Reci, 2020;Borgen et al., 2021). The study’s moderators fall into four groups: (1) Family-related factors (marital status, number of children, proportional contribution to family budget and previous unemployment experience). (2) Job-related factors (total work experience, years in a current position, current monthly salary level, liking a current job from the start and education). (3) Organizational attribute (company size). (4) Demographic characteristics (gender and age). The methodology applied for the research is outlined in the next section, and the application of these moderators is designed to increase the practicality of the study. Methodology Subjective Career Success model has been created. It includes the Savickas and Porfeli (2012) CAAS behaviours: concern, control, curiosity and confidence, and interpersonal behavior (connection) behaviours by Gattiker and Larwood (1986). All statements were measured using the ten-point Likert scale, which has a higher validity and explanatory power than the five-point Likert scale suggested by Coelho and Esteves (2007). Finally, subjective career success was measured in accordance with Gaile et al. (2020) by using two statements: “To what extent are you satisfied with your job?”from Colakoglu (2011),Converse et al. (2014) and Verbruggen et al. (2015), and “I am satisfied with the success of my career”from Greenhaus et al. (1990). A calculated Cronbach’s alpha with a value of 0.768 indicates good internal consistency for the construct of this factor. The study data were collected in April 2020 by inviting people from different backgrounds via social media platforms, such as LinkedIn and Facebook. The questionnaire was written in Latvian, suggesting that the results can be attributed to Latvia’s socioeconomic context. A total of 390 valid responses were obtained. Table 1 details the study sample descriptions. The constructs’reliability and validity were assessed using SPSS 28 (Statistical Package for the Social Sciences). Subsequently, a mathematical model was implemented in WarpPLS 8.0, a user-friendly software package for varianceand factor-based structural equation EJMBE 33,4 414 modeling (SEM) employing the partial least squares method (PLS), as recommended by Hair et al. (2014). PLS-SEM is a widely accepted method for exploratory research in various fields, including management and organizational development (Al-Emran et al., 2018;Kock and Hadaya, 2018). WarpPLS, a software program developed by ScriptWarp Systems, is a powerful tool for predictive PLS-SEM cases rooted in established theories (Hair et al., 2014). Notably, for exploratory research, PLS-SEM is favored (Kock and Hadaya, 2018), and WarpPLS stands out by allowing the explicit identification of nonlinear functions connecting latent variables in SEM models and calculating associated multivariate coefficients of association, a capability unique to this software (Kock, 2010). Unlike other tools that offer solely linear functions, WarpPLS is the first to provide classic PLS algorithms alongside factor-based PLS algorithms for SEM (Kock, 2019). Total number of respondents: 390 # % Gender Female 283 72.56 Male 107 27.44 Age Range 23–67 Mean 40.07 S.D. 9.65 Education Primary school 2 0.51 Secondary school 40 10.26 Bachelor’s degree 104 26.67 Master’s degree 229 58.72 Doctoral degree 15 3.85 Marital status Not married 174 44.62 Married 216 55.38 Children Range 0–5 Mean 1.04 S.D. 1.1 Contribution to family budget (%) Range 10–100 Mean 64.09 S.D. 25.30 Salary level (EUR) 500 or less 0 0.00 501–1,000 101 25.90 1,001–2,000 177 45.38 2,001–5,000 85 21.79 More than 5,000 27 6.92 Company size Solo 25 6.41 Small 106 27.18 Midsized 112 28.72 Large 147 37.69 Liking current job at its start (Likert-10) Range 0–10 Mean 7.82 S.D. 1.76 Years in current position Range 0–44 Mean 5.63 S.D. 6.14 Total work experience (years) Range 0–50 Mean 18.93 S.D. 9.75 Unemployment experience Yes 214 54.87 No 176 45.13 Source(s): Authors Table 1. Study’s sample descriptives Expanding career adaptability 415 Research model Figure 1 presents the initial research model, which contains the interpersonal behavior construct (connections) as well as the four career adaptabilities constructs (control, curiosity, confidence and concern). This reflects the starting point for the research meta-model in this paper and the key constructs for further PLS-SEM analysis. The general PLS-SEM analysis results include model fit and quality indexes: average path coefficient (APC), average R-squared (ARS), average adjusted R-squared (AARS), average block variance inflation factor (AVIF) and average full collinearity VIF (AFVIF). It is recommended that the pvalues (significance) for APC, ARS andAARS should all be equal to or lower than 0.05; this was the case for the main research model (APC 50.220, p<0.001;ARS50.410, p<0.001; AARS 50.402, and p< 0.001). Ideally, both AVIF and AFVIF should be equal to or lower than 3.3 (particularly in models where most of the constructs are measured through two or more indicators); this was true for the model used (AVIF 51.699 and AFVIF 51.863). Measurement model: reliability and validity To ensure the validity and reliability of the reflective measurement model, various tests were conducted using the most frequent techniques according to Ringle et al. (2012). Thus, the internal consistency reliability with Cronbach’s alpha (CA) and composite reliability (CR), the convergent validity with the average extracted variance (AVE) and the discriminant validity with the Fornell–Larcker criterion (Ringle et al., 2012) were tested. Validity indicates the degree to which a measurement model can predict what will be measured. By contrast, reliability checks the degree to which the same measured values lead to the same results; this represents the failure rate (Burns and Burns, 2008;Weiber and M€ uhlhaus, 2014). In the beginning, the internal consistency reliability was tested (for which the average correlation of all of the individual items of the same construct are compared). Thus, this shows the accuracy of a group of variables or items measuring a latent variable. Internal consistency reliability is mostly measured with CA and CR (Ringle et al., 2012). The higher the values of CA and CR, the more congruent the items (ergo, the higher the internal reliability). In the model, the CA of all the constructs was considerably greater than 0.7 (Table 2), which is mentioned as the threshold. The CR should have a value of at least 0.6, which is reflected in the current model values in Table 2 (ranging from 0.855 to 0.903) (Weiber and M€ uhlhaus, 2014). Thus, this model is internally reliable. Figure 1. Subjective career success model of study EJMBE 33,4 416 Regarding testing the convergent validity, AVE was used; this determined the average percentage of the items that explain the dispersion of the latent construct. In the literature, a threshold of 0.5 is mentioned for this factor (Fornell and Larcker, 1981;Bagozzi and Yi, 1988); this was the case for all the constructs in the model (Table 2). Lastly, the discriminant validity with the Fornell–Larcker criterion (Fornell and Larcker, 1981) was checked. Therefore, the square root of each AVE in a diagonal must be compared with the correlation coefficients for each construct. Table 2 shows that the AVE was higher in each case in the model, so the discriminant validity is accepted. Table 3 provides structure loadings and cross-loadings for deeper insights into the construct’s reliability and validity. CR CA AVE PCS CONN CONT CURI CONF CONC PCS 0.896 0.768 0.811 0.901 0.511 0.389 0.114 0.268 0.251 CONN 0.881 0.797 0.712 0.511 0.844 0.353 0.140 0.234 0.248 CONT 0.860 0.803 0.506 0.389 0.353 0.712 0.511 0.562 0.468 CURI 0.855 0.788 0.543 0.114 0.140 0.511 0.737 0.666 0.634 CONF 0.903 0.871 0.609 0.268 0.234 0.562 0.666 0.780 0.461 CONC 0.890 0.844 0.619 0.251 0.248 0.468 0.634 0.461 0.787 Note(s): (CR: composite reliability; CA: Cronbach’s alpha; AVE: average variance extracted) as well as interconstruct correlation matrix (with square roots of AVEs shown in italic on diagonal) Source(s): Authors PCS CONN CONT CURI CONF CONC PCS1 0.901 0.466 0.367 0.113 0.245 0.223 PCS2 0.901 0.456 0.333 0.092 0.238 0.23 CONN1 0.447 0.844 0.355 0.146 0.251 0.239 CONN2 0.458 0.865 0.317 0.108 0.18 0.212 CONN4 0.387 0.821 0.217 0.1 0.16 0.175 CONT1 0.493 0.362 0.632 0.231 0.321 0.423 CONT2 0.292 0.281 0.767 0.41 0.384 0.393 CONT3 0.267 0.273 0.773 0.465 0.539 0.37 CONT4 0.124 0.146 0.699 0.339 0.327 0.236 CONT5 0.267 0.281 0.733 0.386 0.492 0.305 CONT6 0.24 0.164 0.653 0.326 0.312 0.273 CURI1 0.025 0.003 0.337 0.759 0.365 0.458 CURI2 0.116 0.115 0.38 0.809 0.48 0.583 CURI3 0.107 0.163 0.441 0.67 0.445 0.403 CURI4 0.253 0.234 0.46 0.713 0.669 0.407 CURI6 0.077 0.011 0.277 0.726 0.508 0.472 CONF1 0.229 0.222 0.403 0.436 0.739 0.28 CONF2 0.216 0.211 0.42 0.454 0.73 0.242 CONF3 0.173 0.128 0.429 0.635 0.788 0.487 CONF4 0.197 0.114 0.384 0.563 0.779 0.428 CONF5 0.162 0.203 0.461 0.527 0.837 0.35 CONF6 0.283 0.22 0.529 0.499 0.806 0.363 CONC1 0.007 0.069 0.135 0.43 0.212 0.734 CONC2 0.143 0.148 0.276 0.441 0.3 0.753 COCN3 0.234 0.206 0.437 0.52 0.43 0.842 CONC4 0.259 0.218 0.458 0.487 0.353 0.744 CONC5 0.336 0.315 0.508 0.604 0.494 0.851 Source(s): Authors Table 2. Reliability and validity measures Table 3. Structure loadings and cross-loadings Expanding career adaptability 417 Results The study results in this section are presented in three sub-sections. The first describes the main model, as well as the key relationships between the dependent variable of the subjective career success and the independent variables of connections (control, curiosity, confidence and concern). The second outlines the statistically significant moderating effects of the 12 variables (Table 4)on the main relationships in the research model. The third explores how the significant moderating effects altered the relational strengths among the independent and dependent constructs. Main model The main results of the structural model are presented in Figure 2. The βvalues that are noted next to each arrow demonstrate the strength of the relationships among the constructs, and the asterisks mark their statistical significance (the R 2 contributions are presented in brackets). All the paths in the model are statistically significant. The model visibly demonstrates how significantly strong the effect of the connections is on explaining subjective career success (β50.412, p< 0.001). This is especially true when comparing it with the path coefficients of the other four constructs that represent the career adaptability factors (β50.236–0.117, p< 0.001–0.050). The total effects and effect sizes are also provided in Figure 2. The effect sizes (f 2 ) determine whether the effects indicated by the path coefficients are small (0.02), medium (0.15) or large (0.35). This study reveals that relationships at work (connections) dominate in the size of their effect on subjective career success (f 2 50.217) as compared to the effect sizes of the other four constructs (f 2 50.096–0.023) related to the CAAS. The current model in Figure 2 portrays the differences by changing the arrow sizes and thicknesses accordingly. Of the four career adaptability factors, control had the strongest predictive power on subjective career success (β50.236, p< 0.001), with a medium to small effect size (f 2 50.096). The next largest effect on subjective career success (β50.198, p< 0.001) came CONN →PCS CONT →PCS CURI →PCS CONF →PCS CONC →PCS Marital β50.160*** p< 0.001 β50.090* p50.036 Children β50.147** p50.002 β50.099* p50.024 FamilyBudget β50.100* p50.023 Unemployment β50.195*** p< 0.001 YearsTotal β50.094* p50.030 YearsCurrent ––– – – Salary ––– – – LikeStart β50.193*** p< 0.001 Education β50.085* p50.045 β50.104* p50.019 CompanySize β50.107* p50.016 Gender β50.087* p50.042 Age β50.095* p50.028 Source(s): Authors Table 4. Overview of moderating effects in main research model EJMBE 33,4 418 Fernandez, D.P., Ryan, M.K. and Begeny, C.T. (2023), “Gender expectations, socioeconomic inequalities and definitions of career success: a qualitative study with university students”,PLoS One, Vol. 18 No. 2, e0281967, doi: 10.1371/journal.pone.0281967. Fornell, C.R. and Larcker, D.F. 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