Looking Ahead,Looking Around,and Looking to Others: Identifying Core Proactive Behaviors in the Quest for Career Sustainability

Abstract

The topic of proactivity has long captured the attention of career scholars, leading to fertile, yet often disconnected streams of inquiry on personality traits and behaviors that can help workers to advance their own careers, improve their work conditions, or access desired rewards. Based on a review of diverse approaches to conceptualizing and assessing proactive career behavior and related constructs, we identified seven commonly appearing behavioral categories and assembled a representative set of items of each category. An exploratory factor analysis in a sub-sample of adult workers (n = 250) yielded three interrelated factors, labeled (a) planning/reflecting/reskilling (or looking ahead, e.g., engaging in self-reflection and skill development efforts); (b) networking/conferring (or looking to others, e.g., consulting with colleagues and supervisors); and (c) exploring/searching (or looking around, e.g., monitoring career options proactively). A bifactor model fit the data well in another sub-sample (n = 337), suggesting that the three factors were subsumed by a larger construct, which we labeled career sustainability behavior. Results of a structural path analysis indicated that, along with supervisor support, proactive personality, and conceptually-relevant self-efficacy measures, engagement in career sustainability behaviors was predictive of perceived internal and external job marketability.

Keywords

proactive career behavior career sustainability social cognitive career theory career self-management adult workers

Introduction

Career development theorists and researchers have increasingly noted economic and workplace changes that have disrupted many people’s work lives and that are likely to continue to do so over the foreseeable future (e.g., Hirschi, 2018; Lent, 2013, 2018). For example, advances in automation, artificial intelligence, and machine learning, coupled with offshoring of jobs, globalization of industries, and corporate mergers and downsizing, raise serious questions about the long-term employability and career sustainability of large numbers of workers in the U.S. and abroad (Brynjolfsson & McAfee, 2014; Friedman, 2016). In addition, many adults currently engage in precarious, gig, and contract work, often experiencing unsatisfying or exploitive work conditions, low wages, and an absence of benefits (e.g., health care insurance) (Blustein & Duffy, 2021). The COVID pandemic has introduced yet another layer of disruption to the lives of many workers (Akkermans et al., 2020).

Economists differ in their assessments of the seriousness, scope, and intractability of the problems facing workers in the near and long term future. Some point to positive economic prospects and dismiss notions of a “robot apocalypse” (Mishel & Bivens, 2017). Others offer more dire forecasts of increasing joblessness and work instability (Ford, 2015). The resulting uncertainty has made it difficult for career educators, counselors, and coaches to know how best to equip students and clients with strategies to help them chart a course through an uncertain macroeconomic environment.

Researchers in vocational, organizational, and management psychology have responded to this dynamic situation by launching myriad efforts to conceptualize and measure the strategies that workers use to cope with the threat of unstable work while also trying to improve their current job and career prospects. This dual focus on career preservation and advancement is evident, for example, in inquiry on protean and boundaryless careers (Wiernik & Kostal, 2019), adaptability (Savickas & Porfeli, 2012), and career self-management (Lent & Brown, 2013). Much of this inquiry relates to the broad theme of career proactivity (Crant, 2000; Parker & Collins, 2010) which, in line with the concept of proactive coping (Aspinwall & Taylor, 1997), can involve efforts to anticipate and forestall potential challenges to one’s work well-being.

Career Sustainability, Proactivity, and Preparedness

De Vos et al. (2020) have recently proposed a conceptual model of sustainable careers, defined in terms of three broad sets of outcomes: health, happiness, and productivity. The current study, by contrast, is more concerned with career sustainability as an adaptive process (cf. Lent & Brown, 2013), and with the specific behaviors that workers can learn and use to promote self-defined career objectives, especially those related to maintaining employability or marketability. Despite their differential outcome versus process foci, our approach shares with De Vos et al. a view of individuals as active agents who interact with their environments in an ongoing effort to achieve desirable career outcomes; a concern with employability (which is part of De Vos et al.’s definition of productivity); and an emphasis on proactivity in career self-management.

In keeping with Crant (2000), we define proactive behavior as “taking initiative in improving current circumstances or creating new ones; it involves challenging the status quo rather than passively adapting to present conditions” (p. 436). Proactivity is intimately related to the concept of psychological preparedness, defined as “a goal state of readiness to respond to uncertain outcomes. It includes being prepared for possible setbacks should they occur, but also being prepared to take advantage of opportunities when they arise” (Sweeny et al., 2006, p. 302). In the context of career-life development, a healthy state of preparedness confers the potential to respond to “threats to one’s career well-being as well as to… resources and opportunities on which one can capitalize. Most important, preparedness can lead to the use of proactive strategies to manage barriers, build supports, and otherwise advocate for one’s own career-life future” (Lent, 2013, p. 7).

Efforts to understand career proactivity have taken at least three directions, conceiving of proactivity as (a) a personality trait, termed proactive personality (Seibert et al., 1999), (b) a set of learned behaviors, often but not always referred to as proactive career behavior (Strauss et al., 2012), or (c) a collection of knowledge, skills, and capabilities (e.g., career competencies; Akkermans et al., 2013; career agency, Rottinghaus et al., 2012; career adaptability, Savickas & Porfeli, 2012). There have also been a few efforts to understand proactivity within an integrative theoretical framework, taking personality, cognitive, behavioral, and social-contextual variables into account (e.g., Lent et al., 2022). Yet integration has generally not kept pace with efforts at construct and measure proliferation. Though the resulting literature has produced a wealth of data, Crant’s (2000) observation still seems apt: we do not yet have “an integrated research stream …. There is no single definition, theory, or measure driving this body of work; rather, researchers have adopted a number of different approaches toward identifying the antecedents and consequences of proactive behavior, and they have examined them in a number of seemingly disconnected literatures” (p. 435). Indeed, the lack of consensus is not limited to antecedents and consequences; the very meaning and measurement of proactive career behavior also lacks unity.

The Many Guises of Proactive Career Behavior

Proactive career behavior is perhaps the most commonly used umbrella term describing the behaviors that workers employ specifically to protect and/or advance their careers. It has also been used as the name of a particular set of measures (Strauss et al., 2012), variations of which include career initiative behaviors (Parker & Collins, 2010), enacted aspirations (Tharenou & Terry, 1998), occupational engagement (Krieshok et al., 2009), and career engagement (Hirschi et al., 2014). Though these and similar measures go by different names and vary somewhat in their formatting (e.g., verb tenses, scaling formats), examination of their item content suggests a good deal of conceptual overlap. For example, “I am planning what I want to do in the next few years of my career” is an item on the career planning dimension of the Proactive Career Behavior scale (Strauss et al., 2012), while “Developed plans and goals for your future career” is an item on Hirschi et al.’s (2014) Career Engagement scale.

We should note that there is, in fact, a potentially vast list of proactive behaviors that workers may employ to promote their own career growth or benefit their organizations (e.g., see the effort by Parker & Collins, 2010, to classify these behaviors). For example, behaviors such as voice (Van Dyne & LePine, 1998) and taking charge (Morrison & Phelps, 1999) enable people to perform their work and serve their organizations more effectively. Likewise, such behaviors as job change negotiation (Ashford & Black, 1996), job crafting (Wrzesniewski & Dutton, 2001), and self-advocacy (Moturu & Lent, 2022) represent strategies used by individuals to enhance person-environment fit in terms of work tasks, interpersonal relationships, or desired work rewards.

In using the terms proactive career behavior and career-sustaining behavior, we are concerned with behaviors that workers employ chiefly to chart their own career paths and, especially, to protect and maintain their employability, rather than those primarily intended to affect how they perform their jobs or how they are compensated for performing them. However, we recognize that the motivation for, and effects of, diverse proactive behaviors can naturally overlap and, thus, there can be no rigid distinction between behaviors intended to protect one’s career and those intended to produce other outcomes. For example, proactive skill development can serve both career-protecting and performance-enhancing functions, yet our interest here is in how it may be used in self-directed efforts to sustain or enhance one’s employability or marketability for desired jobs and career paths. Many of the same behaviors can also be used proactively or reactively. Thus, behaviors such as skill development or career exploration can be pursued either before or after workers experience threats to their current jobs. However, we are primarily interested here in the proactive or preventive functions of such behaviors.

For current purposes, we are specifically interested in career-protecting behaviors that can potentially be learned or modified via social cognitive means, rather than how one perceives one’s fund of career-related knowledge or potential. That is, we emphasize the “doing versus having” sides of functioning (Cantor & Sanderson, 1999). Our purview of career-sustaining behavior will, therefore, exclude non-behavioral person constructs (e.g., traits, general self-qualities, perceived career knowledge), such as those indexed by the Career Adapt-Abilities Scale (Savickas & Porfeli, 2012), the Career Competencies Questionnaire (Akkermans et al., 2013), and the Career Agency scale (Rottinghaus et al., 2012). We likewise exclude measures that reflect the affordances or outcomes of career sustainability (e.g., enabling a good standard of living; the Career Sustainability Scale; Chin et al., 2022) rather than the behaviors designed to obtain such outcomes. Though the self-attributes and outcomes represented by such measures have great theoretical merit, it is not always clear how people may acquire or strengthen career-sustaining knowledge, traits, skills, and affordances they do not already possess, or translate what they have into what they can do.

An Effort to Organize the Many Guises of Proactive Career Behavior

We took both a rational and empirical approach in an attempt to synthesize diverse conceptualizations of career-sustaining behavior, with the goal of seeking a common framework for research and practice purposes. Our rational approach began with a review of the literature on proactive career behavior, conceptually related behaviors, and their measures (e.g., career initiative, career engagement, occupational engagement, career exploration), including prior reviews of research on how workers seek to actively manage their careers (e.g., Hirschi & Koen, 2021; Parker & Collins, 2010). Our review identified seven frequently appearing constructs that could be considered as reflecting key aspects of career sustainability (i.e., behaviors used to protect, maintain, or extend one’s employability): career planning, proactive skill development, career consultation, network building, self-exploration, environmental exploration, and preparatory job search behavior. The first four aspects are exemplified by the components of Strauss et al.’s (2012) Proactive Career Behavior scale and the earlier measures from which it was derived. Career exploration, often subdivided into self and environmental exploration components (e.g., Stumpf et al., 1983), represents another set of ways that workers seek to manage their career prospects (Hirschi & Koen, 2021; Jiang et al., 2019), that is, by entertaining new jobs or career paths.

We identified preparatory job search behavior as a seventh aspect of career sustainability, though it was not evident to us whether to consider it as distinct from, or a logical extension of, self and environmental exploration. We observed, for example, that Hirschi and Koen (2021) classified exploration and job search as distinct aspects of career self-management, and several measures have operationalized them as such (e.g., Blau, 1994; Stumpf et al., 1983). At the same time, the conceptualization and measurement of exploration and preparatory search behavior often blend together. For example, the job curiosity scale of Krieshok et al.’s (2009) occupational engagement measure includes such items as “I let friends know that I’m open to exploring other jobs” and “I daydream about career possibilities.” Likewise, Hirschi et al.’s (2014) career engagement scale includes the item, “Collected information about employers, professional development opportunities, or the job market in your desired area.”

It can be difficult to know where exploration ends and search begins. There can also be a gray area between exploration as planning and exploration as action (e.g., thinking about a job change vs. taking concrete steps, such as perusing classified ads, to explore openings). Put another way, job curiosity can be manifest cognitively (e.g., reflection) or behaviorally (in observable action) and more or less actively. For example, Blau’s (1994) scale distinguishes between preparing/revising a resume (preparatory search behavior) and sending a resume to prospective employers (active search behavior). Moreover, networking and consultation behaviors can also have exploratory functions; that is, workers sometimes use interpersonal contacts with colleagues and supervisors to explore new job opportunities. While our review suggested that the seven behavioral aspects offer a reasonable initial representation of the career sustainability domain, we ultimately treated their interrelations and structure as an empirical question (i.e., might they function as relatively distinct behavioral strategies or might they be organized under a smaller number of headings?).

We should note that our conceptual scheme overlaps substantially with those of Hirschi and colleagues. For example, in surveying the literature on career self-management, career competencies, and related theoretical concepts and measures, Hirschi et al. (2014) identified six clusters that included a variety of proactive career and exploration behaviors. Similarly, Hirschi and Koen (2021) defined career self-management (including proactive career behavior) in relation to seven behavioral components (e.g., planning, networking, exploration, job search, support-seeking). To distinguish proactive career behavior as a specific measure (Strauss et al., 2012) from the larger fund of measures that cover similar ground and reflect a common concern with maintaining workers’ employability, we will generally use “career sustainability” or career-sustaining behaviors as more inclusive labels.

A Social Cognitive Perspective on Career-Sustaining Behavior

We will use the social cognitive career self-management (CSM) model (Lent & Brown, 2013) as a framework for organizing our examination of the dimensionality of proactive career-sustaining behaviors, their correlates, and presumed outcomes. The CSM model, developed within the framework of social cognitive career theory (Lent et al., 1994), offers a flexible approach to understanding how workers negotiate a wide range of career tasks and challenges (Brown & Lent, 2019). Figure 1 displays the general CSM model. Proactive career-sustaining behavior may be conceptualized as a class of adaptive actions in the model. In our application of the model, we conceive of proactive career-sustaining behavior as being predicted, in part, by trait (e.g., proactive personality) and contextual (e.g., career support) variables and, in turn, predicting career sustainability outcomes, such as marketability or employability (e.g., perceptions about one’s job prospects within and outside of one’s current organization; Eby et al., 2003; Forrier et al., 2015). The linkages among these classes of variables have been examined in prior research. For example, Fuller and Marler (2009) reported significant meta-analytic correlations between proactive personality and proactive (career initiative) behaviors (.35), and Lent et al. (2022) found that a model containing a four-factor representation of proactive career behavior, along with measures of other CSM model constructs, predicted perceived marketability as well as career satisfaction and organizational rewards growth.

The Present Study

The present study seeks to organize and, perhaps, simplify the conceptualization of behaviors that workers engage in, either within or apart from specific jobs, to protect or enhance their career viability. In doing so, we attempt to build on prior efforts to assess proactive and related career behaviors (Hirschi et al., 2014; Strauss et al., 2012) by identifying a reasonably comprehensive set of sustainable behavior themes or categories, clarifying their commonalities and differences, and studying them within a larger theoretical context. To operationalize the seven behavioral categories that, according to our review, represent different though overlapping aspects of career sustainability, we developed an item pool drawn from a variety of existing measures, including proactive career behavior (Strauss et al., 2012), career engagement (Hirschi et al., 2014), occupational engagement (Krieshok et al., 2009), enacted aspirations (Tharenou & Terry, 1998), occupational awareness (Rottinghaus et al., 2012), career exploration (Stumpf et al., 1983), and preparatory job search behavior (e.g., Blau, 1994; Saks & Ashforth, 1999). We should note that we were less interested in how these measures have been labeled than we were in the themes reflected by their item content.

Our specific aims were threefold: (a) to examine the dimensionality of our seven behavioral categories of career-sustaining behavior in a sub-sample of employed workers; (b) to assess the stability of the obtained factor structure in a second sub-sample of employed workers; and (c) to examine the relation of the underlying dimensions to relevant correlates (proactive personality, career support) and presumed outcomes of career sustainability (perceived internal and external job marketability), positioning each of the variables within the framework of the social cognitive CSM model (Lent & Brown, 2013; Lent et al., 2022). Because the seven behavioral categories may well contain overlapping elements (e.g., consultation and networking each represent ways of harnessing interpersonal support), we elected to treat the number and composition of factors as empirical questions rather than imposing a fixed structure on the items. Accordingly, we framed three overarching research questions: (a) What are the dimensions underlying the pool of career-sustaining behavior items? (b) How do these dimensions relate to measures of trait proactivity and contextual (supervisory) support? (c) Though they are generally expected to predict perceived employability criteria (as indexed by internal and external job marketability), which, if any, of them will account for unique predictive variance in these criteria? In pursuing these questions, we hoped to explore the prospects for forging an integrative conception of career-sustaining behaviors and how they function in unison with other person and contextual predictors of employability.

Method

Participants

The sample consisted of 587 adult workers (326 women, 261 men), with a mean age of 38.41 years (SD = 8.08). Participants reported being employed full-time (i.e., at least 40 hr per week) within an organizational setting (i.e., not self-employed). They were in the age range of 25–55 years, which spans Super’s Establishment stage and half of the Maintenance stage (Hartung, 2021). Although each of these developmental periods has distinctive features, they contain the overlapping challenges of initiating and sustaining a viable work path. (We did not include workers over age 55 because of the proximity of some older workers to retirement, which could affect their engagement in career sustainability behaviors, such as networking or ongoing exploration.) In terms of race/ethnicity, 63% were White or European American, 20% Latinx, 10% Black or African American, 4% Asian/Pacific Islander American, 2% multiracial, and less than 1% Native American. They were distributed throughout the U.S., with most living in the South (43%), followed by the West (20%), Northeast (19%), and Midwest (17%). In terms of education, 33% had a 4-year college degree, 24% a graduate or professional degree, 11% a 2-year college degree, and 32% a high school degree. They represented a diverse array of occupations, with the largest groups including information technology (12%), education and training (9%), finance (9%), health (8%), and manufacturing (6%). Annual salaries ranged from less than $15,000 (2%) to $200,000 or more (2%); about half of the sample earned between $25,000 and $74,999 per year. Forty-six percent had held two or more different jobs, and 20% had experienced an involuntary job loss, over the past 5 years. Data were gathered during the second year of the COVID-19 pandemic.

Procedure and Instruments

Participants were obtained via Qualtrics Research Services (QRS) and completed the survey online, for which they received a modest incentive (e.g., credit toward gift cards). QRS-recruited samples have been found to be reasonably representative of U.S. population demographics (Boas et al., 2018). In order to qualify for the sample, respondents needed to complete a university approved informed consent form, indicate that they were at least 25 years old, and currently working at least 40 hr per week. The survey included two validity items, which were used to screen out inattentive respondents. QRS also used bot detection to prevent non-human responses and IP address checks to prevent multiple responses from the same participant. We retained data only from participants who had responded appropriately to the screening mechanisms (e.g., passing both validity items) and who completed all demographic items, allowing us to confirm their fit to the sampling parameters. Total scale scores were calculated by summing item responses and dividing by the number of items on each scale.

We divided the study into two phases. In the first phase, employing a randomly selected sub-sample of 250 of the participants, we explored the factor structure of the proactive career and exploratory behavior item set and examined the initial reliability and validity estimates of the obtained factors. In the second phase (n = 337), we assessed the replicability of the factor structure and tested a CSM-based model predicting internal and external job marketability.

Career Sustainability Behavior

Our review of the literature on proactive career behavior and conceptually related constructs (e.g., career exploration) uncovered a relatively small set of behavioral categories but a large variety of published and unpublished scales linked to those categories, with much overlapping item content. Following a strategy similar to that of Parker and Collins (2010), we sought to preserve construct validity while minimizing survey fatigue, participant frustration, and response bias, especially given the inclusion of additional measures in the study for model testing purposes. We, therefore, adapted 39 items from published scales to represent the seven categories of career-sustaining behavior: career planning (e.g., “I am planning what I want to do in the next few years of my career”; Strauss et al., 2012); proactive skill development (e.g., “I voluntarily participate in further education, training, or other activities to support my career,” Hirschi et al., 2014); career consultation (e.g., “I have discussed my career prospects with someone with more experience in my work organization,” Tharenou & Terry, 1998); network building (e.g., “I maintain lots of contacts with people in my line of work,” Krieshok et al., 2009); self-exploration (e.g., “I have reflected on how my past integrates with my future career,” Stumpf et al., 1983); environmental exploration (e.g., “I keep current with job market trends,” Rottinghaus et al., 2012); and preparatory job search behavior (e.g., “I have revised my resume,” Blau, 1994). To increase the chances of yielding a stable factor structure, each category was represented by five to six items.

Parenthetically, as implied earlier, we assumed that preparatory job search behavior might represent a relatively dynamic form of career exploration (e.g., taking explicit actions to test the job waters vs. monitoring career information more passively). Therefore, we incorporated items referring to aspects of proactive job searching (e.g., letting others know that one is open to exploring other jobs). We did not include items reflecting full immersion in the job search process (e.g., applying for new jobs) because, for current purposes, we were interested in strategies that might be used more generally for proactive rather than reactive purposes (i.e., routine scanning of “what’s out there” vs. more intensive job-finding or career change efforts occasioned by current job insecurity or dissatisfaction).

Item selections and editing were made consensually by the first two authors, both of whom had extensive experience as career development researchers. We began by identifying commonly used measures of each behavioral category (e.g., proactive career behavior; Strauss et al., 2012; career exploration; Stumpf et al., 1983) and then examining the content of additional measures that have been used to reflect conceptually similar constructs. Rather than attempting to include all possible measures of the seven behavioral categories, our rationally based strategy was to include representative items that were expressed in clear behavioral terms, framed in a career or work context, grammatically correct (or easily correctable without altering meaning), and not highly redundant with other selected items.

Because the 39 items we identified had been formatted in a variety of ways (e.g., in terms of verb tense, explicit vs. implicit use of the word “I,” temporal frame, scaling) in their original sources, we edited them to achieve a more consistent style, first person perspective, and a common 5-point scaling format (1 = strongly disagree; 5 = strongly agree). For example, the item “Actively sought to design your professional future” (Hirschi et al., 2014) was modified to read, “I actively try to design my professional future.” Participants were instructed that the measure “asks about the types of things you may (or may not) do to help plan, protect, or advance your career and work options.” They were then asked to indicate the extent to which they agreed with each of the item statements. The items representing the seven categories were dispersed throughout a single, common measure.

Self-Efficacy for Sustainability Behavior

We employed measures of self-efficacy for performing proactive career behavior (Lent et al., 2022), career exploration and decision-making (Lent et al., 2016), and preparatory job search behavior (Saks et al., 2015). This allowed us to roughly parallel the construct domain of the proactive career behavior and exploration measures. Lent et al. (2022) had developed the 12-item proactive career behavior self-efficacy measure to reflect confidence at performing the same four types of behavior (e.g., career planning) represented by the Strauss et al. (2012) proactive career behavior scale. Self-efficacy items were preceded with the stem, “How much confidence do you have in your ability to…” A sample self-efficacy for career planning item was, “Steer your career in the directions you want it to go.” Self-efficacy items were rated on a 5-point scale, from no confidence at all (0) to complete confidence (4). The authors reported that the scale score produced an alpha reliability value of .91 and correlated as expected with proactive career behavior, proactive personality, supervisor support, and indicators of career progress.

The 8-item Lent et al. (2016) scale was used to index self-efficacy at the career exploration and decisional process. Participants are asked to rate their confidence at performing each task (e.g., “Identify careers that best match your interests”) along a 5-point rating scale, from no confidence at all (0) to complete confidence (4). Lent et al. (2016) estimated the internal consistency of this measure as above .90 and found it to yield theory-consistent correlations with exploratory goals, decisional anxiety, and career decidedness. Job search behavior self-efficacy was assessed with six (of 10) items from Saks’ et al.’s (2015) scale reflecting behaviors that could be performed at preparatory (vs. active) phases of the search process (cf. Blau, 1994). Participants indicated their confidence at performing each behavior (e.g., “Conduct information interviews to find out about careers and jobs that you are interested in pursuing”) using a 5-point (1 = no confidence; 5 = complete confidence) rating scale. Saks et al. reported that the full scale yielded an alpha coefficient of .89 and correlated moderately with measures of career planning, exploration, and job search intention and behavior.

Supervisor Support

Support was measured with Greenhaus et al.’s (1990) perceived supervisory support scale. Reflecting the degree of career support workers receive from their immediate supervisor, the scale includes nine items to which participants respond on a scale from 1 (strongly disagree) to 5 (strongly agree). A sample item is “My supervisor takes the time to learn about my career goals and aspirations.” Greenhaus et al. reported that the scale yielded an alpha coefficient of .93 and correlated moderately with career satisfaction, perceived organizational acceptance, and discretion over the performance of one’s job.

Proactive Personality

Proactive personality, the disposition to engage in proactive behavior, show initiative, and persevere at change efforts, was assessed with the 10-item Proactive Personality Scale (PPS; Seibert et al., 1999), a brief version of the original 17-item Bateman and Crant (1993) scale. A sample item is, “I am constantly on the lookout for new ways to improve my life.” Participants rate their degree of agreement with each statement on a 7-point (1 = strongly disagree; 7 = strongly agree) scale. Seibert et al. reported that the short form yielded an alpha estimate of .86, correlated very highly with the original version of the scale, and correlated with career satisfaction and indicators of objective career success.

Job Marketability

We assessed job marketability with two 3-item scales, reflecting participants’ estimates of their marketability both within and outside of their current work organizations (Eby et al., 2003). Sample items include “There are many opportunities available for me in my company” (internal marketability) and “I could easily obtain a comparable job with another employer” (external marketability). Participants indicate their degree of agreement with each item along a strongly disagree (1) to strongly agree (5) scale. Eby et al. reported that scores on the internal and external scales yielded alpha coefficients, respectively, of .73 and .74 and intercorrelated moderately. Both scales were also found to produce small correlations with proactive personality and medium to large correlations with measures of career satisfaction and identity. Using a German version of the two scales, Spurk et al. (2016) reported that they each correlated moderately to strongly and inversely with measures of job and career insecurity.

Career Satisfaction

The 5-item Career Satisfaction scale (Greenhaus et al., 1990), viewed as an indicator of subjective career success (Ng & Feldman, 2014), includes items such as, “I am satisfied with the progress I have made toward meeting my goals for income.” The scale has been found to yield adequate internal consistency estimates and theory-consistent relations with measure of proactive career behavior and job marketability in prior research (e.g., Lent et al., 2022). We used it in the preliminary measure validation phase of the current study, prior to model testing, to examine the utility of the career sustainability behavior factor(s) in predicting internal and external marketability after controlling for prior career success.

Results

Exploratory Factor Analysis

Though we organized the items within seven initial behavioral categories to capture a reasonably wide range of career-sustaining behaviors, we took an exploratory, data-driven approach to factor analysis to allow for other, possibly simpler and unanticipated dimensional structures to emerge. We therefore subjected data from the first sub-sample (n = 250) to principal axis factoring and oblimin oblique rotation. A Kaiser-Meyer-Olkin index of .93 and significant Bartlett’s test of sphericity (p < .001) supported the factorability of the items. Parallel analysis, scree, eigenvalue, and factor interpretability criteria were used to determine factor structure. We retained items that yielded pattern matrix loadings above .40 on a primary factor and minimal cross-loadings on other factors (difference of ≥ .15 between primary and secondary loadings). These criteria were consistent with common guidelines for exploratory factor analysis and have been used in other CSM measure validation studies (e.g., Moturu & Lent, 2022).

A three-factor solution yielded the most plausible factor structure, though seven items either cross-loaded or had primary loadings <.40. We re-ran the analysis after omitting these items and found that the retained items accounted for 47% of the total variance. The first factor (15 items, 33% of the variance) consisted mainly of career consultation and networking items treatment (e.g., “I make my supervisor aware of my work aspirations and goals”; “I am building a network of colleagues I can call on for support”). The second factor included eight items primarily reflecting ongoing career exploration (8% of the variance), such as “I have collected information about employers, professional development opportunities, or the job market” and “I let friends know that I’m open to exploring other jobs.” The third factor (nine items, 6% of the variance), emphasized career planning, self-exploration, and proactive skill development (e.g., “I am thinking ahead to the next few years and plan what I need to do for my career”; “I have reflected on how my past integrates with my future career”; “I gain experience in a variety of areas to increase my knowledge and skills”). We labelled the three factors, respectively, as networking/conferring (or “looking to others” at work for advice, support, and information), exploring/searching (or “looking around” at alternative work possibilities), and planning/reflecting/reskilling (or “looking ahead” to help guide one’s career path). For simplicity, we will generally refer to these factors as networking, exploring, and planning.

In order to produce a reasonably brief set of scales of similar length for future research purposes, we decided to retain only the nine networking items with the highest factor loadings. An additional factor analysis containing only these nine networking items plus all items on the other two factors (total of 26 items) yielded adequate Kaiser-Meyer-Olkin (.91) and Bartlett’s test of sphericity values. The 3-factor solution accounted for 49% of the total variance (networking, 32%; exploring, 10%; planning, 7%). Item-factor loadings, shown in Table 1, ranged between .50 and .72 for the networking factor, .46 and .72 for the exploring factor, and .43 and .67 for the planning factor.

Table 1.

Items and Factor Loadings for the Career Sustainability Variables.

Items/Factors
Networking/Conferring	Net¹	Exp¹	Pln¹	CFA
I make my supervisor aware of my work aspirations and goals^a	.72	.10	−.04	.69
I am building a network of colleagues I can call on for support^a	.70	−.05	−.01	.73
I have discussed my career aspirations with a senior person in my work organization^b	.69	.18	−.06	.70
I initiate talks with my supervisor about training or work assignments I need to develop skills that will help my future work chances^a	.61	.06	.07	.70
I seek advice from my supervisor(s) or colleagues about additional training or experience I need in order to improve my future work prospects^a	.56	.02	.18	.65
I establish or maintain contacts with people who can help me professionally^c	.54	.01	.23	.70
I maintain lots of contacts with people in my line of work^d	.54	−.01	.09	.57
I have discussed my career prospects with someone with more experience in my work organization^b	.51	.13	.12	.71
I am building a network of contacts or friendships to provide me with help or advice that will further my work chances^a	.50	.06	.28	.67
Exploring/Searching
I let friends know that I’m open to exploring other jobs^d	.09	.72	−.07	.66
I have collected information about employers, professional development opportunities, or the job market^c	.20	.67	−.05	.77
I have read a book or article about getting a job or changing jobs^e	.13	.60	−.02	.60
I picture the kind of life I might have with a different career^d	−.20	.54	.09	.49
I have conducted information interviews to find out about careers and jobs that I am interested in pursuing^f	.19	.52	−.06	.62
I keep current with job market trends^g	.17	.51	.02	.74
I daydream about career possibilities^d	−.16	.46	.14	.48
I have obtained information on specific jobs or companies^h	.15	.46	.23	.67
Planning/Reflecting/Reskilling
I am thinking ahead to the next few years and plan what I need to do for my career^a	.04	.03	.67	.71
I have reflected on how my past integrates with my future career^h	−.08	.06	.64	.55
I have investigated career possibilities^h	−.02	.08	.59	.61
I gain experience in a variety of areas to increase my knowledge and skills^a	.29	−.18	.55	.66
I develop knowledge and skill in tasks critical to my future work life^a	.25	−.18	.55	.68
I am planning what I want to do in the next few years of my career^a	.01	.07	.54	.55
I have spent time thinking about my personal values, interests, abilities, and weaknesses in relation to my career^c	.28	.00	.50	.56
I think about the relevance of past behavior for my future career^h	−.03	.29	.44	.60
I develop skills which may not be needed so much now, but in future positions^a	.23	.04	.43	.56
Eigenvalue	8.28	2.62	1.75
% of variance	31.85	10.06	6.72

Note. Net = Networking; Exp = Exploring; Plan = Planning. ¹Item loadings from the pattern matrix of the exploratory factor analysis; primary loadings shown in bold font. CFA = item-factor loadings from the 3-factor confirmatory factor analysis. Original item source: ^aStrauss et al. (2012); ^bTharenou & Terry (1998); ^cHirschi et al. (2014); ^dKrieshok et al. (2009); ^eBlau (1994); ^fSaks & Ashforth (1999); ^gRottinghaus et al. (2012); ^hStumpf et al. (1983).

Initial Reliability and Validity Analyses

Table 2 presents the means, standard deviations, internal consistency values, and correlations among the variables in the first sub-sample. All variables were distributed normally and reliability estimates ranged between .68 (internal marketability) and .93 (supervisor support). Reliability values for scale scores based on the networking, exploring and planning factors were, in order, .88, .81, and .84. (Corresponding values in the second sub-sample were .88, .84, and .84.) The three sustainability scales were moderately to strongly interrelated; for networking with exploring, r = .42; for networking with planning, r = .64; and for exploring with planning, r = .42. They were also related to each of the other predictor and criterion variables in the CSM-derived model, with correlations ranging from small (exploring and supervisor support, r = .15) to large (networking and proactive behavior self-efficacy, r = .67). Exploring tended to yield somewhat smaller correlations with the other variables in the model, relative to planning and networking behavior.

Table 2.

Correlations, Means, Standard Deviations, and Internal Consistency Estimates for Initial Validation Phase.

Variables	1	2	3	4	5	6	7	8	9	10	M	SD	α
1. Planning	--										4.03	.56	.84
2. Exploring	.42	--									3.59	.72	.81
3. Networking	.64	.42	--								3.89	.66	.88
4. Proactive Psnlty	.66	.39	.57	--							5.61	.94	.92
5. Supervisor Supp	.40	.15	.58	.38	--						3.71	.91	.93
6. Explor Self-Eff	.54	.32	.53	.61	.40	--					3.83	.67	.90
7. Proactive Self-Eff	.55	.27	.67	.65	.59	.78	--				3.83	.66	.92
8. Job Srch Self-Eff	.47	.38	.50	.57	.33	.65	.62	--			3.76	.70	.87
9. Int marketability	.45	.20	.48	.51	.60	.53	.57	.43	--		3.87	.73	.68
10. Ext marketability	.46	.31	.43	.50	.24	.48	.49	.45	.45	--	3.98	.65	.72
11. Career Satisf	.36	.08	.41	.35	.52	.48	.57	.39	.55	.34	3.75	.88	.90

Note. N = 250; correlations ≥ .15 are significant, p < .05. Psnlty = Personality; Supp = Support; Explor = Exploratory; Eff = Efficacy; Srch = Search; Int = Internal; Ext = External; Satisf = Satisfaction.

To explore the joint contribution of the three sustainability behavior scales to the prediction of internal and external marketability, we regressed each criterion variable on the following predictors in three successive steps: (a) a set of demographic and employment-related variables, including age, gender (male vs. female), race (white vs. persons of color), income level, education (college graduates vs. non-graduates), job changes over the past 5 years (no vs. yes), and involuntarily unemployment over the past 5 years (no vs. yes); (b) career satisfaction; and (c) the set of networking, exploring, and planning scales. We found that the block of demographic and employment variables did not account for significant variance at the first step of the equation predicting internal marketability, though career satisfaction and the set of sustainability scales explained significant unique variance at the second and third steps (ΔR² = .29 and .10, respectively). Among the sustainability behaviors, only planning (β = .19) and networking (β = .20) yielded significant (p < .05, one-tailed) regression coefficients.

In the equation predicting external marketability, the block of demographic and employment predictors once again did not explain a significant amount of variation at the first step, but career satisfaction (ΔR² = .10) and the set of sustainability behaviors (ΔR² = .16) accounted for significant unique variation at subsequent steps. All three sustainability scales produced significant beta weights (.13, .16, and .24, respectively, for networking, exploring, and planning). Thus, exploring behaviors contributed uniquely to the prediction of external but not internal marketability.

To get a sense of the relative frequency with which the three sustainability behaviors are used, we compared their mean scores (shown in Table 2). Participants reported engaging most often in planning, followed, in order, by networking and exploring behavior. Paired samples tests indicated that the mean difference between planning and exploring was significant (t = 10.14 [249]) and of medium size (Cohen’s d = .64). Planning was also used more often on average than networking (t = 4.26 [249]; Cohen’s d = .27), and networking more often than exploring, (t = 6.49 [249]; Cohen’s d = .41); both of these latter differences are in the small effect size range.

Confirmatory Factor Analyses

Data from the remaining sub-sample (n = 337) were used to confirm the factor structures identified in the first sub-sample. Specifically, we tested three competing measurement models: (a) a 3-factor model in which each item was set to load only on its corresponding factor from the exploratory factor analysis; (b) a single-factor model in which all items were set to load on a common factor; and (c) a bifactor model in which each item was set to load both on its specific factor, as in the 3-factor model test, and on a general factor. The bifactor model was designed to examine the extent to which the set of items reflect a unidimensional versus multidimensional structure. Model testing employed the MLM estimation procedures of Mplus 8.4 (Muthén & Muthén, 2019). Hu and Bentler’s (1999) 2-index method was used to assess adequacy of model-data fit. Using this method, fit may be considered adequate if (a) SRMR values ≤.08 in combination with (b) CFI values ≥.95 or RMSEA values ≤.06.

Fit indices for the one-factor model were non-optimal on two of the three fit criteria: SRMR = .07, RMSEA = .07 (90% CI = .07, .08), CFI = .82; Satorra-Bentler (S-B) χ² (299) = 811.90, p < .001. The 3-factor model produced significantly better fit to the data, SRMR = .06, RMSEA = .05 (90% CI [.05, .06]), CFI = .90; S-B χ² (296) = 579.38, p < .001, ΔS-B χ² (3) = 178.33, p < .001. However, the three latent factors were substantially interrelated, with correlations ranging from .76 to .79. The bifactor model offered improved fit over the 3-factor model, SRMR = .06, RMSEA = .05 (90% CI [.04, .05]), CFI = .93; S-B χ² (273) = 467.80, p < .001, ΔS-B χ² (23) = 106.78, p < .001. All but two items loaded above .40 on the general factor in the bifactor model (range = .29 to .74). The standardized factor loadings of the bifactor model are shown in Table 1.

Using Duebner’s (2017) calculator to compute ancillary bifactor indices, we found an explained common variance (ECV) value for the general factor of .72, compared to .15, .39, and .35, respectively, for the specific networking, exploring, and planning factors; all but four of the item ECV values were >.50 and nine were >.80 (M = .68). The percent of uncontaminated correlations (PUC, .69) neared the recommended cut-off for unidimensionality (.70) and the Omega hierarchical value for the general factor was high (.85); for the specific networking, exploring, and planning factors, this value was, respectively, .04, .34, and .29. According to Reise et al. (2013), with a PUC <.80, an item set may be considered primarily unidimensional if ECV >.60 and Omega hierarchical >.70 for the general factor. On balance, the bifactor indices suggest that a general sustainable career behavior factor underlies the specific networking, exploring, and planning factors.

Testing the Full Measurement and Structural Models

We next included the career sustainability construct, along with the other predictors of perceived marketability, in testing the CSM-based model shown in Figure 2. Each construct was represented at the latent variable level, using scale scores, items, or item parcels to reduce the number of parameter estimates in relation to sample size. Proactive personality and supervisor support were each indexed with three item parcels, following Little et al.’s (2013) balancing method. Internal and external marketability were each modeled with their three corresponding items. Self-efficacy was represented by the proactive career, career exploration and decision, and preparatory job search self-efficacy scales. Consistent with the bifactor model results, we modeled career sustainability as a general construct, using the networking, exploring, and planning scale scores as its observed indicators.

We first tested a measurement model containing six correlated factors. This model yielded good fit to the data, SRMR = .05, RMSEA = .06 (90% CI = .05, .07), CFI = .95, S-B χ² (120) = 279.62, p < .001. Indictor-factor loadings ranged from .58 to .92, and the factors were all significantly interrelated, with latent variable correlations ranging between .41 (supervisor support with external marketability) and .73 (internal marketability with external marketability, self-efficacy with proactive personality). We next tested the degree to which the paths among the constructs conform to the CSM-based structural model (see Figure 2). This model also produced good fit indices, SRMR = .05, RMSEA = .06 (90% CI [.05, .07]), CFI = .95, S-B χ² (122) = 281.29, p < .001.

As hypothesized, supervisor support covaried significantly with proactive personality and both variables were predictive of self-efficacy. Engagement in sustainability behavior was predicted by self-efficacy and supervisor support, though the direct path from proactive personality to behavior was not significant. The paths from self-efficacy and behavior to both internal and external job marketability were significant, with the paths from self-efficacy yielding larger coefficients than those from behavior. Supervisor support contributed significantly to the prediction of internal but not external marketability. The model accounted for substantial amounts of the variance in self-efficacy and behavior as well as in internal and external marketability; R² values, respectively, were .65, .50, .58, and .50).

Finally, we contrasted this model with a simpler variation that represented behavior and self-efficacy only in terms of the four facets of proactive career behavior corresponding to the Proactive Career Behavior scale (Strauss et al., 2012). The aim of this contrast was to examine the extent to which the more complex target model, which includes aspects of career exploration and preparatory job search behavior, could account for additional variance in job marketability relative to the simpler proactive career behavior-only model. In the latter (non-nested) model, behavior and self-efficacy were indexed only by the four sub-scales, respectively, of the Strauss et al. and Lent et al. (2022) measures (the sub-scales of each measure were used as indicators of proactive behavior and self-efficacy factors); the remaining constructs were represented as in the target model. A measurement model yielded adequate fit indices, SRMR = .05, RMSEA = .06 (90% CI [.05, .07]), CFI = .94 S-B χ² (155) = 357.16, p < .001; the structural model likewise provided adequate fit to the data, SRMR = .05, RMSEA = .06 (90% CI [.05, .07]), CFI = .94, S-B χ² (157) = 360.40, p < .001. This subset of the target model accounted for 4% less of the variance in internal marketability and 2% less in external marketability than did the target model.

Discussion

The present study had been designed to identify common dimensions of behaviors that can be used to promote career sustainability, particularly among early to mid-career workers. To capture such behaviors, we assembled representative items from measures of proactive career behavior, career-related self and environment exploration, and preparatory job search behavior. We reasoned that ongoing exploration and preparatory search behaviors offer a set of options for maintaining one’s employability that could complement the networking, career consultation, skill development, and career planning dimensions of proactive career behavior.

An exploratory factor analysis identified three larger dimensions, which we termed networking/conferring, exploring/searching, and planning/reflecting/reskilling. (We refer to them more simply as networking, exploring, and planning, below.) Scale scores based on these factors were moderately (exploring with both networking and planning) to strongly (networking and planning) interrelated. The three scales also correlated with measures of supervisor support, proactive personality, self-efficacy (regarding proactive career behavior, career exploration, and job search behavior), and perceived internal and external marketability. Planning and networking tended to relate moderately to strongly with these other measures, with more modest relations involving exploring behaviors.

Preliminary regression analyses indicated that, controlling for subjective career success and a variety of demographic and employment-related variables, the sustainability behaviors collectively accounted for an additional 10% of the variance in internal marketability and 16% of the variance in external marketability. Among the sustainability behaviors, only planning and networking yielded significant beta weights in the prediction of internal marketability, while all three produced significant beta weights relative to external marketability. It may be that exploring behaviors are experienced as more useful to enhancing one’s employability prospects outside of one’s current work organization, whereas networking may serve a similar function within the organization (in terms of providing access to information about prospective work options). It is also possible that career planning, including reflection and self-directed skill development, is a fundamentally useful sustainability strategy across organizational contexts.

In addition to the correlation and regression findings, we observed that participants displayed mean differences in the extent to which they utilized the three forms of sustainability behavior, with planning most likely to be employed, followed by networking and then exploring. Their differential use may be linked to the time and effort required to perform them (e.g., many planning items involve cognitive processing rather than overt action) or to how individuals experience their work conditions. For example, exploring behaviors may be more likely to be triggered by worries over employment security or by job dissatisfaction than is the case with the other behaviors. While some workers routinely “window shop,” others may explore different work options only in the context of an experienced or anticipated threat to their jobs. In a recent study applying the CSM model to a sample of call center employees, Presbitero and Teng-Calleja (2023) found that fears about the job impact of artificial intelligence were associated with greater job insecurity and distress and, in turn, engagement in career exploration behavior.

Using confirmatory factor analysis to test three alternative representations of career sustainability, we found good fit to the data for a 3-factor model corresponding to the exploratory factor analysis, yet even better fit for a bifactor model. The latter allowed all items to load on both a general factor and on the three specific sustainability factors. Bifactor indices suggested that the item set could be seen as primarily unidimensional; that is, the general factor largely subsumed the three specific factors. Moreover, in the 3-factor model test, the factors all intercorrelated highly (above .7), providing additional evidence that, while they were somewhat differentially predictive of the marketability criteria in our regression analyses with observed variables, they covaried highly at the latent variable level in the second sub-sample.

Given support for the general sustainability factor, we used it in measurement and structural analyses, along with the other CSM model-derived predictors of marketability. These analyses were largely consistent with a 6-factor structure of the predictor and dependent variables and with the hypothesized paths among the variables. In particular, the sustainability behaviors were well-predicted by the combination of supervisor support and self-efficacy beliefs, though proactive personality was linked to sustainability behavior only indirectly, via self-efficacy. Engagement in sustainability behavior in turn was, along with self-efficacy and supervisor support, predictive of internal marketability. Sustainability behavior and self-efficacy, though not supervisor support, were also predictive of external marketability. It may be that participants perceived they would need to rely more on themselves, and less on their supervisors, in locating employment opportunities outside of, as opposed to within, their organizations.

Prior research on proactive career behaviors has yielded four first-order factors (networking, consulting, planning, skill development) and a higher order factor (Strauss et al., 2012). Our findings suggest an overlapping, though somewhat different, structure. Like Strauss et al. and Hirschi et al. (2014), we found support for a larger proactivity (or sustainability) factor. However, we also found three more specific factors: (a) networking and consulting, (b) planning and skill developing, and (c) exploring and searching. Strauss et al. had not included exploring and searching behaviors in operationalizing proactivity; though Hirschi et al. had done so, they may not have included enough such items to yield a distinct factor. Our findings also echo those of Lent et al. (2022), who recently found that proactive behavior and self-efficacy were each linked to job marketability. We extended their findings, however, by (a) measuring proactive behaviors in broader terms; (b) dividing marketability into more specific internal and external organizational facets; and (c) demonstrating that the expanded measurement of career proactivity may account for additional variance in internal and external marketability.

Implications for Future Research and Practice

While the proliferation of measures of proactive career behavior and similar constructs (e.g., agency, engagement, involvement, exploration) has spawned a great deal of inquiry, it may also have contributed to the perception that research on career proactivity consists of many disconnected streams that ultimately fail to converge (Crant, 2000). In fact, one may reasonably ask, what does it mean to be proactive in the career realm? Proactivity for what purpose? What are the various forms of proactivity and similar behaviors designed to do, and what unique or common benefits do they have?

Our primary goal was to further the search for connection among the seemingly diverging streams of inquiry on career proactivity. The findings, which require replication, suggest that the representative items we assembled formed three basic, highly related factors. While it may be useful to distinguish them for conceptual and training/counseling purposes, we found that they tend to covary to such a degree that they may be infused by a more general career sustainability factor. Thus, they may be seen as a complementary set of behaviors; those who tend to be planful in charting out a career path or developing their work skills also tend to network with, and seek career advice and support from, their coworkers and supervisors. In addition, they may engage in some measure of proactive career exploration and job searching.

Though they form a coherent whole, these three forms of sustainability behavior may not be entirely redundant or used interchangeably in practice. Our mean level findings suggest that planning, which includes elements of self-exploration, reflection, and self-directed skill development, may be the most basic or common form of career sustainability. Theoretically, planning can be seen as a building block that aids workers to organize their efforts at networking and exploring by fostering a sense of career direction – though the imagined destination may be more or less clear to the individual, ranging from fuzzy distal aspirations to clear proximal goals. Networking, the second most performed behavior in our sample, may represent an effort to elaborate or implement one’s plan. Exploration may also reflect plan implementation but it was used least often, perhaps because it may require the most effort (e.g., reading, contacting non-work colleagues, scanning for job openings) outside of one’s current work time. For some workers, exploration may also reflect concerns with job satisfaction or stability rather than constituting a more routine form of proactive behavior.

Our findings regarding the added predictive utility of the expanded operationalization of proactive/sustainability behaviors raise reasonable cost-benefit considerations. On the one hand, this operationalization did account for additional predictive variance relative to the four original proactive career behaviors alone (Strauss et al., 2012). On the other hand, one may ask whether the modest gain in explanatory utility (4% and 2% additional variance in internal and external marketability, respectively) is sufficient to offset the slight loss of parsimony. (The Strauss et al. scale contains roughly half the number of items as the full set of sustainability items.) The potential costs associated with exploring (e.g., time, effort) might also be considered. Yet for workers grappling with poor person-environment fit or uncertain job security, the potential benefits (e.g., cushioning against unemployment) may be worth the costs.

The study’s findings should be interpreted in the context of additional caveats. For example, the concept of career sustainability behavior presupposes that it is advantageous to empower workers to exercise agency to help protect their careers, remain employed, and seek advancement opportunities. However, because individual agency is subject to environmental constraints, it is not always possible to anticipate job layoffs or to insure access to desired work rewards. Still, while they cannot guarantee employability, sustainability behaviors may serve a useful role by enabling workers to do what they can to forestall unemployment and enhance their own marketability, rather than relying exclusively on the good will of their current employer. Indeed, many workers have boundaryless or protean careers that are delinked from a single organizational context and, therefore, may well wish to invest in their own career development, consistent with the notion of “Me Incorporated” (cf. Hesketh, 2000).

One direction for future inquiry on career sustainability behavior would be to examine its relation to employability in the context of jobs that can be more and less responsive to personal agency, for example, work that requires higher versus lower levels of education or work tasks that are more versus less vulnerable to advances in technology (Presbitero & Teng-Calleja, 2023). It may be that sustainability behaviors are more likely to enhance employability in fields representing “decent work” (Blustein & Duffy, 2021). Alternatively, sustainability efforts may be even more useful for workers facing less stable employment prospects, including those who need or wish to perform temporary or contract work. Sustainability behaviors should also be studied in the context of human and social capital factors (e.g., adequacy of job performance and social networks) that can complement and moderate their relation to employability. Likewise, it might be useful to study patterns of career sustainability behavior in workers at earlier and later stages of career development and in part-time versus full-time workers.

Other caveats that could be addressed in future research include the study’s cross-sectional design, which precludes causal inferences; its use of an online research panel, which raises questions about self-selection bias and generalizability (e.g., the sample had a higher proportion of college graduates than the U.S. population); and its reliance on participant self-reports. These considerations point to the need for replication and extension research employing longitudinal designs and educationally and occupationally diverse samples. While perceived marketability offers one useful window on employability, behavioral measures (e.g., actual job tenure, shorter periods of unemployment subsequent to involuntary job loss) could provide yet more compelling evidence of the utility of sustainability behaviors. In addition, it would be useful to examine the replicability of the factor structure we obtained when utilizing alternative measures of proactivity or sustainability (e.g., whole scales vs. representative items).

At a practical level, the findings suggest career preparedness strategies that can be used either on the job (e.g., networking with colleagues) or beyond the job (e.g., routine exploration) as safety nets against career shocks (e.g., job layoffs) and as routes for identifying attractive work options proactively. Intervention studies designed to promote career sustainability behaviors and self-efficacy beliefs could be used to inform practical efforts intended to prevent or shorten periods of unemployment. Offered by educational institutions and government entities (e.g., via the O*NET), their focus could be on the systematic, ongoing use of career sustainability behaviors, not unlike physical exercise and periodic wellness checks. The social cognitive CSM model could also be mined for relevant intervention elements (e.g., coping models can be used to offer ongoing instruction and support at navigating a boundaryless career).

In sum, the present findings suggest the viability of a bifactor conception of career proactivity. This structure includes specific planning, networking, and exploring elements embedded within a larger career sustainability factor. At the same time, these fundamental behaviors do not exhaust the ways in which workers may seek to protect and advance their careers. Such behaviors as job crafting, self-advocacy, and organizational citizenship offer workers additional means for fitting themselves to the work environment and vice versa. Continued study of the multiple forms of sustainability may inform interventions aimed at facilitating - career self-management and, in particular, forestalling or circumscribing periods of unemployment in vulnerable workers. Finally, research on career sustainability might also include a focus on complementary non-work strategies, such as use of automated savings plans (Thaler & Sunstein, 2021) as hedges against work instability. Such inquiry could locate career sustainability behaviors within a larger career-life preparedness perspective (Lent, 2013).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Robert W. Lent

Steven D. Brown

Ruogu J. Wang

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