In today’s global labour context, where employees are no longer confined to working primarily from the office but can work from anywhere, it is crucial to explore how the shifts in work–life dynamics have reshaped the trajectory towards a prosperous career. Remote work is a broad concept (Morganson et al., 2010), from which a variety of categories have emerged, namely satellite offices, telework, mobile work, informal work, work from anywhere and flexible workplaces (Tavares et al., 2020). Specifically, it refers to the practice of working away from the conventional office space using telecommunications equipment and/or computer-based technology (Bailey & Kurland, 2002; Gajendran & Harrison, 2007).

Before the COVID-19 pandemic, remote work was not widely prevalent, and even individuals who engaged in telework did so infrequently (Ipsen et al., 2021). In 2019, only 15% of employees in the 27 European Union countries (EU–27) had some experience with telework, with 11% being dependent employees and 36% being self-employed (Milasi et al., 2021). Globally, the extent of telework varied between 2% and 40%, depending on factors such as the type of work, sector, country and frequency (Green et al., 2020).

In 2020, due to the COVID-19 pandemic, millions of workers were mandated to transition to remote work, many of whom had never experienced teleworking before. As of April 2020, 40% of employed individuals in Europe transitioned to full-time teleworking (Milasi et al., 2021). Among European countries, Spain, Greece and Poland fell slightly below the regional average at approximately 30%, while France, Portugal and Italy closely matched the average at around 40–45%. Conversely, Luxembourg, the Netherlands and Finland surpassed the average, with rates ranging from approximately 58% to 60% (EuroFound, 2020). Remote work was considered the optimal approach to curbing the transmission of the COVID-19 virus while preserving employment opportunities and minimizing layoffs. However, it is crucial to comprehend the challenges faced by workers in this particular circumstance (Satici et al., 2020).

Some of the risks associated with remote work include the potential exclusion of certain groups from the labour market, such as low-skilled workers, older individuals and those in place-bound occupations. Other risks include increased work intensity and stress, expectations of 24/7 availability, long working hours with limited rest time, blurred boundaries between work and personal life leading to conflicts, information overload, social and professional isolation, reduced communication and teamwork, conflicts arising from a lack of coordination, and high demands for self-management and self-organization (Arntz et al., 2020; Bonacini et al., 2021; Pennington & Stanford, 2020). In Portugal, a study conducted by Tavares et al. (2020) revealed that 40.9% of new teleworkers found the transition to remote work relatively easy. However, contrary to expectations from previous research, productivity levels did not increase (Ipsen et al., 2021). The study found that 44% of employees faced difficulties in meeting deadlines due to inadequate time management skills, resulting in increased working hours and days (Tavares et al., 2020). Another study conducted during the first lockdown with Portuguese adults in a telecommuting situation reported increased feelings of isolation and loneliness due to reduced human interaction, challenges in separating work life from personal and family life, and difficulties in maintaining a structured work routine due to interruptions from family members, including children and pets (Rebordão, 2021). These challenges have particularly affected women, who face time constraints from household responsibilities, leading to the perception of carrying a ‘double-shift’ while working remotely (Arntz et al., 2020; Feng & Savani, 2020; Hartig et al., 2007; Reichelt et al., 2021).

In terms of career advancement, employees often hold the belief that remote work can negatively impact their prospects for career growth and progression (Tavares et al., 2020). Several scholars, such as Bloom et al. (2015), have suggested that this work arrangement decreases the likelihood of career advancement, partly due to the conventional perception that productivity assessment is linked to the amount of time spent on the job (Green et al., 2020; Possenriede et al., 2014). Baruch (2000) points out that the performance of teleworkers may vary based on how their colleagues and managers perceive their employment status and mode of work, which can contribute to a negative perception that remote work may hinder future career prospects. Additionally, employees express concerns that the lack of social interaction in the workplace impedes mentorship, coaching opportunities and their sense of corporate identity (de Vries et al., 2019; Tavares et al., 2020). In summary, employees believe that visibility plays a role in mediating their chances of being considered for promotions.

This study aims to explore the current strategic career behaviours of Iberian remote workers. To achieve this goal, we will adopt the Kaleidoscopic Career Model (KCM; Sullivan & Mainiero, 2008) as our conceptual framework. The KCM draws upon the metaphor of a kaleidoscope – an optical device consisting of a small tube containing colorful glass fragments. As external light reflects upon the mirrors within the tube, it creates captivating visual patterns that change with each movement (Mainiero & Sullivan, 2006). By employing this model, we seek to gain insights into the dynamic and visually stimulating nature of remote workers’ career paths in the Iberian context. By applying this concept to the realm of careers, the parameters of the KCM model undergo transformations over the course of one’s professional journey, giving rise to diverse career patterns characterized by three strategic career behaviours: (i) authenticity, alignment between organizational and personal values; (ii) balance, between professional and non-professional responsibilities; and (iii) challenge, stimulus at work and career progression opportunities. These three parameters operate concurrently throughout an individual's life, albeit with varying degrees of influence on decision-making or career transitions depending on the specific circumstances at any given moment. In response to these situations, the parameters of the kaleidoscope model adapt accordingly. One parameter takes precedence, moving to the forefront and intensifying as it becomes the primary focus in that particular context. Meanwhile, the other two parameters recede into the background, but they remain present and active, as each factor is essential in shaping the current trajectory of a person’s life or career (Sullivan et al., 2009). Regarding remote workers, these behaviours are expected to be even more relevant due to the proactive and autonomous role expected from them, not only in redefining their professional roles and responsibilities but also in terms of making career decisions (Raabe et al., 2007; Wrzesniewski & Dutton, 2001). Strategic career behaviours have been associated with desirable personal and professional outcomes, including perceived success (Sturges et al., 2002), a sense of control over career goals (King, 2000), wage increases and promotions (Lau & Pang, 2000; Raabe et al., 2007), career and job satisfaction (King, 2000; Lent & Brown, 2006; Raabe et al., 2007), and overall life satisfaction (King, 2000). Research also indicates that sociocognitive factors, such as self-efficacy beliefs, perceptions of support and barriers to goal attainment (King, 2000; Kossek et al., 1998; Lent & Brown, 2006; Raabe et al., 2007), and perceived organizational support (Desrosiers, 2001), serve as motivators for engaging in strategic career actions.

Despite the extensive research conducted on this topic, there is still a limited global understanding of the relationships between these variables. Furthermore, the findings from these studies primarily apply to ‘traditional’ workers. The literature suggests that remote workers, in general, perceive that working from home hinders their progress towards desired professional goals (Chung et al., 2021; Hite & McDonald, 2020; McCloskey & Igbaria, 2003; Possenriede et al., 2014). Additionally, as previous studies have indicated (Peters et al., 2016), the extent to which workers embrace formal remote work practices may vary based on cultural values at the national level, indicating that workers from different countries may require different career management strategies. Consequently, this paper aims to analyse the nature, antecedents and consequences of strategic career behaviour in Iberian remote workers.

Research aims and hypothesis

The main goal of this study is to test an integrative model that seeks to explain the nature (authenticity, balance and challenge), antecedents and consequences of career management strategies of Iberian remote workers (see Fig. 1). The intent is to gather relevant data to support the development of career management programmes for employees struggling with the achievement of their career goals as a result of remote working. For that end, the objectives of this study are: (a) to characterize the use of the strategies included in the Kaleidoscope Career Model (KCM) by Iberian remote workers; (b) to test the relationship between the variables that determine the KCM (antecedents) – beliefs of self-efficacy, desire for career control and perceived organizational support – and the use of those strategies; (c) to test the relationships between the variables determined by the KCM (consequences) – perceived career control, subjective career success and career satisfaction – and the use of the career management strategies.

Figure 1
figure 1

Conceptual framework for the kaleidoscope career model, and its antecedents and consequences on remote workers.

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Therefore, based on the main goals set for this study, the following hypotheses were developed:

Hypothesis 1a

Perceived self-efficacy, desire for career control and perceived organizational support predict strategic career behaviours (paths a–c).

Hypothesis 1b

Strategic career behaviours predict perceived career control, objective career success, subjective career success and career satisfaction (paths h–k).

Hypothesis 1c

When controlling for perceived self-efficacy, desire for career control and perceived organizational support, only strategic career behaviours predict perceived career control, objective career success, subjective career success and career satisfaction.

Hypothesis 2

Perceived organizational support predicts perceived self-efficacy and career satisfaction (paths d, g).

Hypothesis 3

Perceived self-efficacy predicts subjective career success (path f).

Hypothesis 4

Objective career success is associated with perceived career control and subjective career success (paths m, o).

Hypothesis 5

Subjective career success is associated with career satisfaction (path p).

Hypothesis 6

Career satisfaction is associated with perceived career control and objective career success (paths l, n).

Methods

Participants

A total of 96 participants (60.4% women) concluded the survey, of which 57.3% were Portuguese and 42.7% were Spanish. The participants’ age ranged from 18 to 63 years (M = 34.52; SD = 11.07). Most participants were single (50%) or divorced (32.1%), and 70.2% of the respondents did not have children. Regarding the highest level of education attained, 27.1% of respondents completed a bachelor's degree, while 41.7% had completed a master’s degree. Most participants reported working full-time (77.1%), as well as their partners (80.7%). Remote work was not a common practice among the respondents until the onset of COVID-19, given that 69% of respondents started for the first time during lockdown. For those working remotely before COVID-19, 65.4% of them did it between 1 and 3 days per week, consisting of 1–4 hours (47.8%) or 5–8 hours (39.2%). With the implementation of the first lockdown, the frequency (and intensity) of remote work rose significantly, as 79.5% of Portuguese respondents started daily, and 38.9% of Spain respondents started 4–6 or 2–3 days per week. The average amount of time spent in remote work was equally dispersed among the two countries, with 59.7% of participants working remotely 5–8 hours per day. Most participants (41.9%) lived 1–5 km away from their conventional office space. Furthermore, the most predominant industry in both samples was education (31.7% for Spain and 27.3% for Portugal). Portuguese respondents reported, on average, higher salary growth, with 73.9% stating an increase in the last 6 years, as opposed to only 61.8% of Spaniards. Half of the respondents did not receive any promotion (56.3%), while 35.0% either received one or two promotions during that period.

Data collection

Data were collected through an online survey distributed in both Portuguese and SpanishFootnote 1 via email and social platforms such as LinkedIn, Facebook and Instagram. The survey included introductory information about the research and a concise definition of remote work, followed by a request for informed consent. By agreeing to participate in the study, participants confirmed that they were above 18 years old, residing in Portugal or Spain, and employed by a European company. Participants who did not meet these criteria were excluded from the sample. After providing consent, participants were asked to answer nine sociodemographic questions, including their country of residence, gender, age, education level, industry of work, employment status, marital status and, if applicable, the employment status of their partner, as well as the number of children they have. Additionally, participants were presented with two control questions to determine if they had previously experienced or were currently engaged in remote work, followed by four questions to characterize their work regime (number of days, number of hours, distance of work and workload). Objective measures of career success, such as salary growth and number of promotions, were included at the end of this section. The subsequent section consisted of self-report instruments selected for each variable. On average, it took participants approximately 10 minutes to complete the entire survey.

Instruments

The online survey encompassed questions on sociodemographic data and on strategic career behaviours (KCM), their antecedents and consequences. This section provides information regarding the measurement of each variable (see Table 1 for descriptive statistics and reliability indices).

Table 1 Antecedents, nature and consequences of strategic career behaviours: variables characterization
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Perceived self-efficacy (Whitely et al., 1991): 11 items (e.g., “Please indicate the extent to which you agree or disagree with each of the following statements: When I make plans for my career, I am confident I can make them work; If I can’t do a job the first time, I keep trying until I can’), using a five-point Likert-type scale (1 = strongly agree, to 5 = strongly disagree). Some items were reverse-coded so that higher values represent higher perceived self-efficacy.

Desire for Career Control (King, 2000): seven items (e.g., ‘Please indicate how important it is for you to have control over: Which employer you work for; The hours you work’), using a five-point Likert-type scale (1 = not at all important, to 5 = extremely important).

Perceived Organizational Support (Eisenberger et al., 1986): 11 items (e.g., ‘Please indicate the extent to which you agree or disagree with each of the following statements: The organization values my contribution to its wellbeing; The organization fails to appreciate any extra effort from me’), using a five-point Likert-type scale (1 = strongly agree, to 5 = strongly disagree). Some of the items were reverse-coded so that higher values represent higher perceived organizational support.

Kaleidoscope Career Model (Sullivan & Mainiero, 2008): 15 items – 5 per subscale (e.g., ‘Please indicate the extent to which each of the following statements describes you: I hunger for greater spiritual growth in my life; I constantly arrange my work around my family needs; I continually look for new challenges in everything I do’), using a five-point Likert-type scale (1 = this does not describe me at all, to 5 = this describes me very well).

Perceived Career Control (Kuijpers & Scheerens, 2006): five items (e.g., ‘Please indicate the extent to which you agree or disagree with each of the following statements: I can make clear career plans; I know what I want to have achieved in my career a year from now’), using a five-point Likert-type scale (1 = strongly agree, to 5 = strongly disagree).

Objective Career Success (Whitely et al., 1991): measured through salary growth (as a percentage) and number of promotions received, controlled for 6 years.

Subjective Career Success (Turban & Dougherty, 1994): four items (e.g., ‘Please indicate the extent to which you agree or disagree with each of the following statements: Up to now my career is very successful; Compared to my co-workers, my career is successful’), using a five-point Likert-type scale (1 = strongly agree, to 5 = strongly disagree).

Career Satisfaction (Greenhaus et al., 1990): five items (e.g., ‘Please indicate the extent to which you agree or disagree with each of the following statements: I am satisfied with the success I have achieved in my career; I am satisfied with the progress I have made toward meeting my overall career goals’). These items have a five-point Likert-type scale (1 = strongly agree, to 5 = strongly disagree).

Data analysis

We based our selection of statistical tests (parametric versus non-parametric) and sample size for hypothesis testing on the Central Limit Theorem. The Central Limit Theorem is a fundamental theorem in statistics which states that, regardless of the underlying distribution (normal or non-normal), the sampling distribution of the mean tends to approach normality as the sample size increases. In a random sample of size n from a population, the standardized sample mean converges to a standard normal distribution. This implies that the sample mean is approximately normally distributed, with a mean equal to the population mean and a standard deviation equal to the population standard deviation divided by the square root of the sample size n. This theorem is particularly relevant for inferential statistics, which rely on the assumption of normality, especially in the social sciences where many variables are not normally distributed. Hence, it is often recommended to have a sample size of at least 30 observations to approximate a normal sampling distribution for the sample mean (Islam, 2018, p.6). Additionally, we also calculated the achieved power based on our alpha level, sample size and predicted effect size. We set the alpha level at 5% (p < 0.05) to detect a small to moderate effect size (⍴^2 = 0.2). With a total sample size of 96 observations and 3 predictors, our achieved statistical power was 98% (conventionally considered 80%).

The collected data was entered into an SPSS database. Prior to computing each variable, we assessed their internal consistency using Cronbach’s alpha, which is reported in Table 1. To examine the hypotheses, we conducted Pearson’s correlation coefficient tests to measure the strength of the associations between career behaviour strategies (mediator variable, MeV) and the antecedent and consequent variables. Linear regression analyses were performed to test the complete theoretical model and estimate the magnitude of the individual relationships between the variables. Additionally, a mediation analysis using Hayes’ process model 4 was conducted to evaluate whether the predictors (perceived self-efficacy, desire for career control and perceived organizational support) indirectly influenced the dependent variables (perceived career control, objective career success, subjective career success and career satisfaction) through the mediator variable (career behavior strategies). Statistical significance was determined at p < 0.05.

Results

Association between strategic career behaviours, antecedents and consequences

Regarding the variables of interest in the model (Fig. 2), strategic career behaviours significantly correlate with perceived self-efficacy (r = −0.45, p < 0.01) and with a desire for career control (r = 0.44, p < 0.01). However, there is no correlation between perceived organizational support and career management strategies. In terms of the association between the strategic career behaviours and the DVs (perceived career control, objective career success, subjective career success and career satisfaction), there is only one statistically significant correlation, that of career satisfaction (r = −0.35, p < 0.01). Therefore, there is no support to test the predictions made in H1a (paths a–c) or H1b (paths h–k).Footnote 2

Figure 2
figure 2

Antecedents and consequences of strategic career behaviours: associations. The black lines correspond to anticipated associations between variables that were confirmed, the grey lines correspond to non-anticipated associations between variables that were confirmed and the dashed lines indicate unconfirmed associations between variables

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Perceived organizational support positively correlates with perceived self-efficacy (r = 0.38, p < 0.01) and with career satisfaction (r = 0.33, p < 0.01), which confirms H2 (paths d, g). Perceived self-efficacy correlates with desire for career control (r = −0.38, p < 0.01), with subjective career success (r = 0.24, p < 0.05) and with career satisfaction (r = 0.56, p < 0.01), but it does not significantly correlate with the other DVs (perceived career control and objective career success). The correlation between self-efficacy and subjective career success confirms H3 (path f). Desire for career control does not correlate with any of the DVs (perceived career control, objective career success, subjective career success and career satisfaction). Perceived organizational support significantly correlates with objective career success (r = −0.34, p < 0.01), but it does not correlate with the other two DVs. Data revealed a significant correlation between self-efficacy and career satisfaction, and between perceived organizational support and objective career success, neither of which were anticipated in the model. Regarding objective career success, it only moderately correlates with subjective career success (r = 0.27, p < 0.05), therefore, there is insufficient evidence to support H4, which also informs us that there is insufficient evidence to support H6Footnote 3. Career satisfaction has a statistically significant relationship with subjective career success (r = 0.53, p < 0.01), thus confirming the association predicted by H5 (Table 2).

Table 2 Antecedents, nature and consequences of strategic career behaviours: correlations between variables
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These findings provide evidence to support the relationships predicted in H2, H3 and H5.Footnote 4 The results also shed light on the lack of significant relationships between the strategic career behaviours and the DVs (perceived career control, objective career success and subjective career success). Given that career management strategies (KCM) are correlated only with career satisfaction, we only included that dependent variable in the mediation analysis.

Predictions between antecedents and consequences of strategic career behaviours

Regression analyses were conducted to test whether perceived organizational support predicts perceived self-efficacy, career satisfaction and objective career success, to provide evidence to support H2 (Table 3). Results for organizational support and perceived self-efficacy showed that the regression model was statistically significant, F (1, 71) = 12.05, p < 0.01. The perceived organizational support explains 15% (R2 = 0.15) of the perceived self-efficacy variance (β = 0.38, p < 0.01). The results for organizational support and career satisfaction showed that the regression model was statistically significant, F (1, 67) = 8.01, p < 0.01. The predictor variables explain 11% (R2 = 0.11) of variance in career satisfaction (β = 0.33, p < 0.01). The results for perceived organizational support and objective career success were also statistically significant, F (1, 71) = 9.41, p < 0.01. The predictor variables explain 12% (R2 = 0.12) of variance of career satisfaction (β = −0.34, p < 0.01).

Table 3 Perceived organizational support as the predictor variable
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To provide evidence to support H3, a regression analysis was conducted on perceived self-efficacy to test whether it predicts subjective career success (Table 4). Results showed that the regression model was statistically significant, F (1, 67) = 4.21, p < 0.01. Self-efficacy explains 6% (R2 = 0.06) of variance of subjective career success (β = 0.24, p < 0.05). Results for perceived self-efficacy and desire for career control were statistically significant, F (1, 72) = 11.87, p < 0.01. Self-efficacy explains 14% (R2 = 0.14) of the desire for career control variance (β = −0.38, p < 0.01). Therefore, lower perceived self-efficacy increases the desire for career control. Finally, perceived self-efficacy predicts career satisfaction, given the unanticipated correlation between both variables. Results showed that the regression model was statistically significant, F(1, 67) = 30.82, p < 0.001. Proportionally, self-efficacy explains 32% (R2 = 0.32) of variance of career satisfaction (β = 0.56, p < 0.001).

Table 4 Perceived self-efficacy as the predictor variable
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Strategic career behaviours: mediation analysis

The findings from the correlation coefficient tests suggest testing a mediation analysis only for career satisfaction. Career management strategies (KCM), as mediator variable, only significantly correlates with career satisfaction, therefore invalidating testing the mediation model for the other three dependent variables in the model. Thus, using Model 4 (PROCESS, Hayes, 2013), perceived self-efficacy, desire for career control and perceived organizational support were entered as predictors, with career management strategies as the mediator variable and career satisfaction as the dependent variable.

The relationship between the predictors and the mediator are presented in Table 5. Perceived self-efficacy significantly predicted strategic career behaviours (b = −0.47, SE = 0.18, t(68) = −2.66, p < 0.05, 95% CI −0.82, −0.12). Desire for career control significantly predicted strategic career behaviours (b = 0.49, SE = 0.16, t(68) = 2.95, p < 0.01, 95% CI 0.16, 0.82). However, the same pattern did not appear for perceived organizational support: the relationship was not statistically significant (b = −0.04, SE = 0.08, t(68) = −0.47, p = 0.64, 95% CI −0.21, 0.13).

Table 5 Relationships between strategic career behaviours and antecedents
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Considering the previous association between career management strategies and career satisfaction, this relationship was also tested (Table 6). Strategic career behaviours did not predict career satisfaction (b = −0.15, SE = 0.14, t(68) = −1.09, p = 0.28, 95% CI −0.44, 0.13). Furthermore, neither predictor, desire for career control and perceived organizational support, significantly predicted career satisfaction (b = 0.10, SE = 0.20, t(68) = 0.50, p = 0.62, 95% CI −0.30, 0.50; b = 0.12, SE = 0.10, t(68) = 1.20, p = 0.24, 95% CI −0.08, 0.31). Data show that when we control for perceived self-efficacy, the mediator stops determining variance in career satisfaction. Consequently, the indirect effect was not statistically significant (indirect effect = 0.07, SE = 0.07, p > 0.05, 95% CI −0.06, 0.24); therefore, H1cFootnote 5 is not confirmed (Table 7).

Table 6 Strategic career behaviours: effects on career satisfaction
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Table 7 Indirect effects of antecedents on career satisfaction
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Discussion

The present study aimed to empirically test an integrative model that explores the causes and consequences of using career self-management strategies, based on the Kaleidoscope Career Model (Sullivan & Mainiero, 2008), among Iberian remote workers.

Perceived organizational support predicts perceived self-efficacy and career satisfaction (H2, paths d, g). Positive assessments by the organization imply that future efforts will also be rewarded (Desrosiers, 2001; Eisenberger et al., 1986). Therefore, when individuals are rewarded for their work through organizational support, they develop more positive beliefs about their capabilities to perform tasks. This is particularly important for remote workers, as self-efficacy levels predict motivation towards challenging work (King, 2000; Kossek et al., 1998; Raabe et al., 2007; Van Vianen et al., 2008), which is often the case for newcomers in remote work settings. Perceived organizational support also plays a crucial role in a remote worker’s satisfaction, as the perception of trust and support, along with policies that protect remote workers, directly impact their wellbeing and mental health (Desrosiers, 2001), consequently enhancing career and life satisfaction. Although not explicitly represented in the model, a correlation between perceived organizational support and desire for career control was expected. Employees are more resistant to supporting changes made by their organization, such as remote work, if they perceive those changes to be beyond their control (Eisenberger et al., 1986). Given that the desire for career control increases with the perception of a loss of control (Brehm & Brehm, 1981; King, 2004), it was anticipated that both variables would correlate.

Perceived self-efficacy predicts subjective career success (H3, path f). This finding is consistent with the career literature, as high self-efficacy is associated with increased motivation and job performance (King, 2000; Tannenbaum et al., 1991), which, in turn, influences self-referent criteria and other referent criteria. These criteria are used by individuals to form perceptions of subjective career success (Heslin, 2005). Perceived self-efficacy also correlates with the desire for career control (path e). King (2000, 2004) argues that when career control leads to positive career outcomes, the perception of self-efficacy increases, subsequently enhancing the desire for career control. However, contrary to our expectations, the results indicate a negative correlation (r = −0.38, p < 0.01). The most plausible explanation for this finding is that individuals with a high perception of self-efficacy already believe they have a high level of career control, leading to a reduced desire for further career control. This explanation relies on the premise that perceived self-efficacy predicts perceived career control, which is not supported by our results.

Self-efficacy also correlates with career satisfaction, which was not initially anticipated in our model. However, this correlation is supported by the career literature, as career satisfaction is considered an internally defined career outcome (Greenhaus et al., 1990, p. 69), and a high perception of self-efficacy is associated with high productivity and motivation (Tannenbaum et al., 1991), both of which are positive career outcomes. The results from the regression analysis further revealed that perceived self-efficacy not only predicted career satisfaction but accounted for 32% of its variance, surpassing the contribution of any other predictor variable in the model.

The lack of mediation of career management strategies between predictors and career satisfaction may be attributed to the characteristics of the respondents. For example, a significant portion (29.2%) of the study sample works in education, an industry heavily impacted by the pandemic due to the shift to online teaching. We speculate that the high presence of education professionals in the sample influenced their perceived self-efficacy, considering the mediating effect of digital technologies on their productivity (e.g. adapting to new teaching methods using ICT tools) (Pozo et al., 2021). In this context, higher self-efficacy may lead to a reduced emphasis on the challenge parameter of the Kaleidoscope Career Model (KCM). Furthermore, individuals tend to focus on the balance parameter of the KCM when they struggle to establish boundaries between work and family life (Sullivan & Mainiero, 2008). Given that 50% of the participants were single and 70.2% had no children, we speculate that this mediates the use of career management strategies, particularly in terms of achieving balance. The absence of correlations between the chosen predictor variables and the dependent variables, as mediated by career management strategies, may also be influenced by external factors such as increased workload due to the pandemic, lack of social support (compared with organizational support) and increased social isolation.

Although the present study encountered challenges in establishing the effect between predictor variables and dependent variables mediated by career management strategies, it offers valuable insights into the evolving dynamics of career self-management resulting from remote work. Future research should investigate the newly identified correlations in the data. Specifically, it would be beneficial to test a new model that considers perceived self-efficacy as a predictor of career satisfaction and perceived organizational support as a predictor of objective career success. Furthermore, future research should include individuals who are not currently engaged in remote work or have never teleworked to determine whether the findings from this study are specific to remote workers.

As a result of these additional studies, practical recommendations can be derived for departments and stakeholders of companies, considering the unique cultural aspects, industry characteristics and workers’ career goals and perceptions of success. Human resource departments can play a crucial role in empowering workers and instilling a sense of control over their career goals, thereby promoting greater acceptance of remote work. As digital career development and advancement become increasingly essential, it is crucial to invest in this area, particularly in countries such as Portugal and Spain, where a significant portion of corporate businesses may require additional training to adapt to the realities of remote work.

Conclusions

The present study aimed to develop an integrative model that explores the causes and consequences of using career self-management strategies, based on the Kaleidoscope Career Model, among Iberian teleworkers. However, the results obtained from this study do not provide sufficient evidence to predict the use of career management strategies or explain their consequences on the career success and satisfaction of Iberian remote workers, except for a few correlations among the predictor variables. Nevertheless, the data collected offered insights into the impact of perceived self-efficacy and desire for career control on the adoption of career management strategies, which align with existing literature (e.g. King, 2000; Kossek et al., 1998; Raabe et al., 2007). Specifically, the data indicated that higher perceived self-efficacy negatively predicts the need to acquire new skills and knowledge, thus reducing the adoption of career management strategies. Additionally, the data revealed that remote workers who desire more career control are more likely to engage in strategic career behaviours.

Furthermore, the results shed light on correlations between the predictor variables and dependent variables, including two unanticipated correlations: perceived self-efficacy with career satisfaction and perceived organizational support with objective career success. These findings are relevant for understanding the career behaviours of remote workers.

In light of the COVID-19 pandemic, which has led to a surge in remote work (McCloskey & Igbaria, 2003; Possenriede et al., 2014), future career management models should consider this new wave of remote work.